% This file was created with JabRef 2.6.
% Encoding: Cp1252

@STRING{CVPR = {Proc. of Computer Vision and Pattern Recognition}}

@STRING{ECCV = {Proc. of European Conf. on Computer Vision}}

@STRING{ICCV = {Proc. of Int'l Conf. on Computer Vision}}

@STRING{IJCV = {Int'l Journal of Computer Vision}}

@STRING{JOSA = {Journal of the Optical Society of America}}

@STRING{PAMI = {IEEE Trans. on Pattern Analysis and Machine Intelligence}}

@STRING{SIGGRAPH = {Proc. of ACM SIGGRAPH}}

@STRING{SIGGRAPHA = {Proc. of ACM SIGGRAPH ASIA}}

%sparse coding survey
@article{ChengSRL2013,
 author = {Cheng, Hong and Liu, Zicheng and Yang, Lu and Chen, Xuewen},
 title = {Sparse Representation and Learning in Visual Recognition: Theory and Applications},
 journal = {Signal Process.},
 issue_date = {June, 2013},
 volume = {93},
 number = {6},
 month = jun,
 year = {2013},
 issn = {0165-1684},
 pages = {1408--1425},
 numpages = {18},
 acmid = {2445949},
 publisher = {Elsevier North-Holland, Inc.},
 address = {Amsterdam, The Netherlands, The Netherlands},
 keywords = {Sparse representation, Sparse subspace learning, Sparsity Induced Similarity, Structured sparsity, Visual recognition},
}

@inproceedings{ZhangGLXA13,
  author    = {Tianzhu Zhang and
               Bernard Ghanem and
               Si Liu and
               Changsheng Xu and
               Narendra Ahuja},
  title     = {Low-Rank Sparse Coding for Image Classification},
  booktitle = {{IEEE} International Conference on Computer Vision, {ICCV} 2013, Sydney,
               Australia, December 1-8, 2013},
  pages     = {281--288},
  year      = {2013},
}

@inproceedings{YangYGH09,
  author    = {Jianchao Yang and
               Kai Yu and
               Yihong Gong and
               Thomas S. Huang},
  title     = {Linear spatial pyramid matching using sparse coding for
               image classification},
  booktitle = {CVPR},
  year      = {2009},
  pages     = {1794-1801},
}

@inproceedings{YanW09,
  author    = {Shuicheng Yan and
               Huan Wang},
  title     = {Semi-supervised Learning by Sparse Representation},
  booktitle = {SDM},
  year      = {2009},
  pages     = {792-801},
}

@article{ChengYYFH10,
  author    = {Bin Cheng and
               Jianchao Yang and
               Shuicheng Yan and
               Yun Fu and
               Thomas S. Huang},
  title     = {Learning With L1-Graph for Image Analysis},
  journal   = {IEEE Transactions on Image Processing},
  volume    = {19},
  number    = {4},
  year      = {2010},
  pages     = {858-866},
}



@article{BelkinNS06,
  author    = {Mikhail Belkin and
               Partha Niyogi and
               Vikas Sindhwani},
  title     = {Manifold Regularization: A Geometric Framework for Learning
               from Labeled and Unlabeled Examples},
  journal   = {Journal of Machine Learning Research},
  volume    = {7},
  year      = {2006},
  pages     = {2399-2434},
}

@inproceedings{Zheng04,
  author    = {Xin Zheng and
               Deng Cai and
               Xiaofei He and
               Wei{-}Ying Ma and
               Xueyin Lin},
  title     = {Locality preserving clustering for image database},
  booktitle = {Proceedings of the 12th {ACM} International Conference on Multimedia},
  address   = {New York, NY},
  pages     = {885--891},
  year      = {2004}
}


@book{plummer1986,
  title={Matching Theory},
  author={Plummer, D. and Lov{\'a}sz, L.},
  series={North-Holland Mathematics Studies},
  year={1986},
  publisher={Elsevier Science}
}

@misc{Asuncion07 ,
	author = "A. Asuncion, D.J. Newman",
	year = "2007",
	title = "{UCI} Machine Learning Repository",
	institution = "University of California, Irvine, School of Information and Computer Sciences"
}

@ARTICLE{Zheng11,
         AUTHOR = {Miao Zheng and Jiajun Bu and Chun Chen and Can Wang and Lijun Zhang and Guang Qiu and Deng Cai},
         TITLE = {Graph Regularized Sparse Coding for Image Representation},
         JOURNAL = {IEEE Transactions on Image Processing},
         YEAR = {2011},
         volume = {20},
         number = {5},
         pages = {1327-1336},
}

@inproceedings{BelkinN01,
  author    = {Mikhail Belkin and
               Partha Niyogi},
  title     = {Laplacian Eigenmaps and Spectral Techniques for Embedding
               and Clustering},
  booktitle = {NIPS},
  year      = {2001},
  pages     = {585-591},
}

@inproceedings{LeeBRN06,
  author    = {Honglak Lee and
               Alexis Battle and
               Rajat Raina and
               Andrew Y. Ng},
  title     = {Efficient sparse coding algorithms},
  booktitle = {NIPS},
  year      = {2006},
  pages     = {801-808},
}

@article{Boyd2011,
 author = {Boyd, Stephen and Parikh, Neal and Chu, Eric and Peleato, Borja and Eckstein, Jonathan},
 title = {Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers},
 journal = {Found. Trends Mach. Learn.},
 issue_date = {January 2011},
 volume = {3},
 number = {1},
 month = jan,
 year = {2011},
 pages = {1--122},
 numpages = {122},
 acmid = {2185816},
 publisher = {Now Publishers Inc.},
 address = {Hanover, MA, USA},
}



@inproceedings{LiuCH10,
  author    = {Jialu Liu and
               Deng Cai and
               Xiaofei He},
  title     = {Gaussian Mixture Model with Local Consistency},
  booktitle = {AAAI},
  year      = {2010},
}

@ARTICLE{HeX11,
author={Xiaofei He and Deng Cai and Yuanlong Shao and Hujun Bao and Jiawei Han},
journal={Knowledge and Data Engineering, IEEE Transactions on},
title={Laplacian Regularized Gaussian Mixture Model for Data Clustering},
year={2011},
month={Sept},
volume={23},
number={9},
pages={1406-1418},
keywords={Gaussian distribution;Laplace equations;expectation-maximisation algorithm;graph theory;pattern clustering;Gaussian distribution;LapGMM;Laplacian regularized Gaussian mixture model;conditional probability distribution;data clustering;data manifold geodesies;expectation-maximization algorithm;nearest neighbor graph model;parameter estimation;Clustering algorithms;Data models;Geometry;Laplace equations;Manifolds;Nearest neighbor searches;Probability distribution;Gaussian mixture model;clustering;graph laplacian;manifold structure.},
}

@book{Chung1997,
  added-at = {2009-02-05T15:34:40.000+0100},
  author = {Chung, F. R. K.},
  description = {This monograph is an intertwined tale of eigenvalues and their use in unlocking a thousand secrets about graphs. The stories will be told --- how the spectrum reveals fundamental properties of a graph, how spectral graph theory links the discrete universe to the continuous one through geometric, analytic and algebraic techniques, and how, through eigenvalues, theory and applications in communications and computer science come together in symbiotic harmony....},
  interhash = {0f0fd754924d4dd54bc185bd1c71d00b},
  intrahash = {95ef10b5a69a03d8507240b6cf410f8a},
  keywords = {graph spectral theory},
  publisher = {American Mathematical Society},
  timestamp = {2009-02-05T15:34:40.000+0100},
  title = {Spectral Graph Theory},
  year = 1997
}

@inproceedings{Ng01,
  author    = {Andrew Y. Ng and
               Michael I. Jordan and
               Yair Weiss},
  title     = {On Spectral Clustering: Analysis and an algorithm},
  booktitle = {Advances in Neural Information Processing Systems (NIPS)},
  address   = {Vancouver, Canada},
  pages     = {849--856},
  year      = {2001}
}



@article{Fraley02,
    abstract = {{Cluster analysis is the automated search for groups of related observations in a dataset. Most clustering done in practice is based largely on heuristic but intuitively reasonable procedures, and most clustering methods available in commercial software are also of this type. However, there is little systematic guidance associated with these methods for solving important practical questions that arise in cluster analysis, such as how many clusters are there, which clustering method should be used, and how should outliers be handled. We review a general methodology for model-based clustering that provides a principled statistical approach to these issues. We also show that this can be useful for other problems in multivariate analysis, such as discriminant analysis and multivariate density estimation. We give examples from medical diagnosis, minefield detection, cluster recovery from noisy data, and spatial density estimation. Finally, we mention limitations of the methodology and discuss recent developments in model-based clustering for non-Gaussian data, high-dimensional datasets, large datasets, and Bayesian estimation.}},
    address = {Department of Statistics, University of Washington, Box 354322, Seattle, WA 98195, United States},
    author = {Fraley, Chris and Raftery, Adrian E.},
    citeulike-article-id = {2109572},
    citeulike-linkout-0 = {http://dx.doi.org/10.1198/016214502760047131},
    citeulike-linkout-1 = {http://www.scopus.com/record/display.url?view=extended\&origin=resultslist\&eid=2-s2.0-0035998835},
    comment = {initialization via model-based hierarchical agglomerative clustering, maximum likelihood estimation via expectation-maximization (EM) algorithm, and the selection of the model and the number of components using approximate Bayes factors with the Bayesian information criterion (BIC) approximation},
    journal = {Journal of the American Statistical Association},
    keywords = {0note, mclust, statistics},
    month = jun,
    number = {458},
    pages = {611--631},
    posted-at = {2007-12-14 01:24:14},
    priority = {0},
    title = {{Model-Based Clustering, Discriminant Analysis, and Density Estimation}},
    volume = {97},
    year = {2002}
}

@book{Bertsekas1999,
    abstract = {{This extensive rigorous texbook, developed through instruction at MIT, focuses on nonlinear and other types of optimization: iterative algorithms for constrained and unconstrained optimization, Lagrange multipliers and duality, large scale problems, and the interface between continuous and discrete optimization.   Among its special features, the book:  1) provides extensive coverage of iterative optimization methods within a unifying framework  2) provides a detailed treatment of interior point methods for linear programming  3) covers in depth duality theory from both a variational and a geometrical/convex analysis point of view  4) includes much new material on a number of topics, such as neural network training, discrete-time optimal control, and large-scale optimization  5) includes a large number of examples and exercises detailed solutions of many of which are posted on the internet  Much supplementary/support material can be found at the book's web page  http://www.athenasc.com/nonlinbook.html}},
    author = {Bertsekas, Dimitri P. and Bertsekas, Dimitri P.},
    citeulike-article-id = {1859441},
    citeulike-linkout-0 = {http://www.amazon.ca/exec/obidos/redirect?tag=citeulike09-20\&amp;path=ASIN/1886529000},
    citeulike-linkout-1 = {http://www.amazon.de/exec/obidos/redirect?tag=citeulike01-21\&amp;path=ASIN/1886529000},
    citeulike-linkout-2 = {http://www.amazon.fr/exec/obidos/redirect?tag=citeulike06-21\&amp;path=ASIN/1886529000},
    citeulike-linkout-3 = {http://www.amazon.jp/exec/obidos/ASIN/1886529000},
    citeulike-linkout-4 = {http://www.amazon.co.uk/exec/obidos/ASIN/1886529000/citeulike00-21},
    citeulike-linkout-5 = {http://www.amazon.com/exec/obidos/redirect?tag=citeulike07-20\&path=ASIN/1886529000},
    citeulike-linkout-6 = {http://www.worldcat.org/isbn/1886529000},
    citeulike-linkout-7 = {http://books.google.com/books?vid=ISBN1886529000},
    citeulike-linkout-8 = {http://www.amazon.com/gp/search?keywords=1886529000\&index=books\&linkCode=qs},
    citeulike-linkout-9 = {http://www.librarything.com/isbn/1886529000},
    day = {01},
    edition = {2nd},
    howpublished = {Hardcover},
    keywords = {nonlinear-programming, optimization, programming},
    month = sep,
    posted-at = {2008-02-29 19:10:33},
    priority = {2},
    publisher = {Athena Scientific},
    title = {{Nonlinear Programming}},
    year = {1999}
}

@article{Fraley02,
    abstract = {{Cluster analysis is the automated search for groups of related observations in a dataset. Most clustering done in practice is based largely on heuristic but intuitively reasonable procedures, and most clustering methods available in commercial software are also of this type. However, there is little systematic guidance associated with these methods for solving important practical questions that arise in cluster analysis, such as how many clusters are there, which clustering method should be used, and how should outliers be handled. We review a general methodology for model-based clustering that provides a principled statistical approach to these issues. We also show that this can be useful for other problems in multivariate analysis, such as discriminant analysis and multivariate density estimation. We give examples from medical diagnosis, minefield detection, cluster recovery from noisy data, and spatial density estimation. Finally, we mention limitations of the methodology and discuss recent developments in model-based clustering for non-Gaussian data, high-dimensional datasets, large datasets, and Bayesian estimation.}},
    address = {Department of Statistics, University of Washington, Box 354322, Seattle, WA 98195, United States},
    author = {Fraley, Chris and Raftery, Adrian E.},
    citeulike-article-id = {2109572},
    citeulike-linkout-0 = {http://dx.doi.org/10.1198/016214502760047131},
    citeulike-linkout-1 = {http://www.scopus.com/record/display.url?view=extended\&origin=resultslist\&eid=2-s2.0-0035998835},
    comment = {initialization via model-based hierarchical agglomerative clustering, maximum likelihood estimation via expectation-maximization (EM) algorithm, and the selection of the model and the number of components using approximate Bayes factors with the Bayesian information criterion (BIC) approximation},
    issn = {0162-1459},
    journal = {Journal of the American Statistical Association},
    keywords = {0note, mclust, statistics},
    month = jun,
    number = {458},
    pages = {611--631},
    posted-at = {2007-12-14 01:24:14},
    priority = {0},
    title = {{Model-Based Clustering, Discriminant Analysis, and Density Estimation}},
    volume = {97},
    year = {2002}
}

@book{Bishop06,
 author = {Bishop, Christopher M.},
 title = {Pattern Recognition and Machine Learning (Information Science and Statistics)},
 year = {2006},
 publisher = {Springer-Verlag New York, Inc.},
 address = {Secaucus, NJ, USA},
}

@ARTICLE{HeLap2011,
author={Xiaofei He and Deng Cai and Yuanlong Shao and Hujun Bao and Jiawei Han},
journal={Knowledge and Data Engineering, IEEE Transactions on},
title={Laplacian Regularized Gaussian Mixture Model for Data Clustering},
year={2011},
month={Sept},
volume={23},
number={9},
pages={1406-1418},
keywords={Gaussian distribution;Laplace equations;expectation-maximisation algorithm;graph theory;pattern clustering;Gaussian distribution;LapGMM;Laplacian regularized Gaussian mixture model;conditional probability distribution;data clustering;data manifold geodesies;expectation-maximization algorithm;nearest neighbor graph model;parameter estimation;Clustering algorithms;Data models;Geometry;Laplace equations;Manifolds;Nearest neighbor searches;Probability distribution;Gaussian mixture model;clustering;graph laplacian;manifold structure.},
}

@inproceedings{ZhouCLLH09,
  author    = {Xi Zhou and
               Na Cui and
               Zhen Li and
               Feng Liang and
               Thomas S. Huang},
  title     = {Hierarchical Gaussianization for image classification},
  booktitle = {ICCV},
  year      = {2009},
  pages     = {1971-1977},
}

@INPROCEEDINGS{YanPatch08,
author={Shuicheng Yan and Xi Zhou and Ming Liu and Hasegawa-Johnson, M. and Huang, T.S.},
booktitle={Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on},
title={Regression from patch-kernel},
year={2008},
month={June},
pages={1-8},
keywords={Gaussian processes;maximum likelihood estimation;pose estimation;regression analysis;synchronisation;CHIL head pose database;Gaussian mixture models;Kullback-Leibler divergence;YAMAHA aging database;head pose estimation;human age;inter-modality similarity synchronization;kernel regression;maximum a posteriori adaptation;patch-based regression framework;Aging;Estimation error;Face;Feature extraction;Head;Humans;Image databases;Kernel;Performance evaluation;Spatial databases},
}

@inproceedings{Povey10,
  author    = {Daniel Povey and
               Lukas Burget and
               Mohit Agarwal and
               Pinar Akyazi and
               Kai Feng and
               Arnab Ghoshal and
               Ondrej Glembek and
               Nagendra K. Goel and
               Martin Karafi{\'a}t and
               Ariya Rastrow and
               Richard C. Rose and
               Petr Schwarz and
               Samuel Thomas},
  title     = {Subspace Gaussian Mixture Models for speech recognition},
  booktitle = {ICASSP},
  year      = {2010},
  pages     = {4330-4333},
}

@book{Duda2000,
 author = {Duda, Richard O. and Hart, Peter E. and Stork, David G.},
 title = {Pattern Classification (2Nd Edition)},
 year = {2000},
 publisher = {Wiley-Interscience},
}

@article{BelkinN03,
  author    = {Mikhail Belkin and
               Partha Niyogi},
  title     = {Laplacian Eigenmaps for Dimensionality Reduction and Data
               Representation},
  journal   = {Neural Computation},
  volume    = {15},
  number    = {6},
  year      = {2003},
  pages     = {1373-1396},
}

@article{GaoTC13,
  author    = {Shenghua Gao and
               Ivor Wai-Hung Tsang and
               Liang-Tien Chia},
  title     = {Laplacian Sparse Coding, Hypergraph Laplacian Sparse Coding,
               and Applications},
  journal   = {IEEE Trans. Pattern Anal. Mach. Intell.},
  volume    = {35},
  number    = {1},
  year      = {2013},
  pages     = {92-104},
}

% optimization

@article{BoltePAL2014,
 author = {Bolte, J{\'e}r\^{o}me and Sabach, Shoham and Teboulle, Marc},
 title = {Proximal Alternating Linearized Minimization for Nonconvex and Nonsmooth Problems},
 journal = {Math. Program.},
 issue_date = {August    2014},
 volume = {146},
 number = {1-2},
 month = aug,
 year = {2014},
 pages = {459--494},
 numpages = {36},
 acmid = {2650169},
 publisher = {Springer-Verlag New York, Inc.},
 address = {Secaucus, NJ, USA},
 keywords = {47J25, 49M27, 49M37, 65K10, 90C26, 90C30, Alternating minimization, Block coordinate descent, Gauss-Seidel method, Kurdyka---\Lojasiewicz property, Nonconvex-nonsmooth minimization, Proximal forward-backward, Sparse nonnegative matrix factorization},
}
% optimization
@article{Bolte2014,
year={2014},
issn={0025-5610},
journal={Mathematical Programming},
volume={146},
number={1-2},
title={Proximal alternating linearized minimization for nonconvex and nonsmooth problems},
author={Bolte, Jérôme and Sabach, Shoham and Teboulle, Marc},
pages={459-494},
publisher={Springer Berlin Heidelberg},
keywords={Alternating minimization; Block coordinate descent; Gauss-Seidel method; Kurdyka–Łojasiewicz property; Nonconvex-nonsmooth minimization; Proximal forward-backward; Sparse nonnegative matrix factorization; 90C26; 90C30; 49M37; 65K10; 47J25; 49M27},
language={English}
}

@inproceedings{BaoJQS14,
  author    = {Chenglong Bao and
               Hui Ji and
               Yuhui Quan and
               Zuowei Shen},
  title     = {{L0} Norm Based Dictionary Learning by Proximal Methods with Global
               Convergence},
  booktitle = {2014 {IEEE} Conference on Computer Vision and Pattern Recognition,
               {CVPR} 2014, Columbus, OH, USA, June 23-28, 2014},
  pages     = {3858--3865},
  year      = {2014},
}


%OMP and L0
@article{Tropp04,
  author    = {Joel A. Tropp},
  title     = {Greed is good: algorithmic results for sparse approximation},
  journal   = {{IEEE} Transactions on Information Theory},
  volume    = {50},
  number    = {10},
  pages     = {2231--2242},
  year      = {2004},
}

@INPROCEEDINGS{Hyder09,
author={Hyder, M. and Mahata, K.},
booktitle={Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on},
title={An approximate L0 norm minimization algorithm for compressed sensing},
year={2009},
month={April},
pages={3365-3368},
keywords={computational complexity;minimisation;signal processing;sparse matrices;NP-hard problem;approximate L0 norm minimization algorithm;compressed sensing;direct lscr0 norm minimization problem;lscr0 norm based signal recovery;sparse signal recovery;Australia;Compressed sensing;Compression algorithms;Computer science;Convergence;Delay;Minimization methods;Sparse matrices;Transform coding;Working environment noise;ℓ0 minimization;ℓ1 minimization;Compressive Sensing;high dimensional signal.;nonconvex optimization;random matrices},
}

%%subspace and SSC
@ARTICLE{Vidal11,
author={Vidal, R.},
journal={Signal Processing Magazine, IEEE},
title={Subspace Clustering},
year={2011},
volume={28},
number={2},
pages={52-68},
month={March},
keywords={computer vision;face recognition;image motion analysis;image segmentation;pattern clustering;affine subspace;computer vision;face clustering problem;high-dimensional data set clustering;linear subspace;motion segmentation;subspace clustering;Clustering algorithms;Data models;Noise;Polynomials;Principal component analysis;Signal processing algorithms;Subspace constraints},
}

@article{Soltanolkotabi2012,
author = "Soltanolkotabi, Mahdi and Candés, Emmanuel J.",
fjournal = "The Annals of Statistics",
journal = "The Annals of Statistics.",
month = "08",
number = "4",
volume = "40",
year = "2012",
pages = "2195--2238",
publisher = "The Institute of Mathematical Statistics",
title = "A geometric analysis of subspace clustering with outliers",
}

@inproceedings{ElhamifarV11,
  author    = {Ehsan Elhamifar and
               Ren{\'{e}} Vidal},
  title     = {Sparse Manifold Clustering and Embedding},
  booktitle = {Advances in Neural Information Processing Systems                 (NIPS)}, 
  address   = {Granada, Spain},
  pages     = {55--63},
  year      = {2011}
}


@inproceedings{ElhamifarV09,
  author    = {Ehsan Elhamifar and
               Ren{\'e} Vidal},
  title     = {Sparse subspace clustering},
  booktitle = {CVPR},
  year      = {2009},
  pages     = {2790-2797},
}

@article{ElhamifarV13,
  author    = {Ehsan Elhamifar and
               Ren{\'{e}} Vidal},
  title     = {Sparse Subspace Clustering: Algorithm, Theory, and Applications},
  journal   = {{IEEE} Trans. Pattern Anal. Mach. Intell.},
  volume    = {35},
  number    = {11},
  pages     = {2765--2781},
  year      = {2013},
}

@article{Dyer13a,
  author    = {Eva L. Dyer and
               Aswin C. Sankaranarayanan and
               Richard G. Baraniuk},
  title     = {Greedy feature selection for subspace clustering},
  journal   = {J. Mach. Learn. Res.},
  volume    = {14},
  number    = {1},
  pages     = {2487--2517},
  year      = {2013}
}

@article{Liu12,
 author = {Liu, Guangcan and Lin, Zhouchen and Yan, Shuicheng and Sun, Ju and Yu, Yong and Ma, Yi},
 title = {Robust Recovery of Subspace Structures by Low-Rank Representation},
 journal = {IEEE Trans. Pattern Anal. Mach. Intell.},
 issue_date = {January 2013},
 volume = {35},
 number = {1},
 month = jan,
 year = {2013},
 pages = {171--184},
 numpages = {14},
 acmid = {2412936},
 publisher = {IEEE Computer Society},
 address = {Washington, DC, USA},
 keywords = {Low-rank representation, subspace clustering, segmentation, outlier detection},
}

@inproceedings{Wang13-lrr-ssc,
  author    = {Yu{-}Xiang Wang and
               Huan Xu and
               Chenlei Leng},
  title     = {Provable Subspace Clustering: When {LRR} meets {SSC}},
  booktitle = {Advances in Neural Information Processing Systems (NIPS)},
  address   = {Lake Tahoe, NV},
  pages     = {64--72},
  year      = {2013}
}


@inproceedings{WangX13,
  author    = {Yu{-}Xiang Wang and
               Huan Xu},
  title     = {Noisy Sparse Subspace Clustering},
  booktitle = {Proceedings of the 30th International Conference on Machine Learning (ICML)},
  address   = {Atlanta, GA},
  pages     = {89--97},
  year      = {2013}
}


@article{soltanolkotabi2014,
author = "Soltanolkotabi, Mahdi and Elhamifar, Ehsan and Candès, Emmanuel J.",
fjournal = "The Annals of Statistics",
journal = "Ann. Statist.",
month = "04",
number = "2",
pages = "669--699",
publisher = "The Institute of Mathematical Statistics",
title = "Robust subspace clustering",
volume = "42",
year = "2014"
}

@inproceedings{Wang16-graphconnectivity,
  author    = {Sreangsu Acharyya and
               Joydeep Ghosh},
  title     = {Parameter Estimation of Generalized Linear Models without Assuming
               their Link Function},
  booktitle = {Proceedings of the Eighteenth International Conference on Artificial
               Intelligence and Statistics (AISTATS)},
  address   = {San Diego, CA},
  year      = {2015}
}


@inproceedings{YouRV16-OMP,
  author    = {Chong You and
               Daniel P. Robinson and
               Ren{\'{e}} Vidal},
  title     = {Scalable Sparse Subspace Clustering by Orthogonal Matching Pursuit},
  booktitle = {Proceedings of the 2016 {IEEE} Conference on Computer Vision and Pattern Recognition (CVPR)},
  address   = {Las Vegas, NV},
  pages     = {3918--3927},
  year      = {2016},
}

%My L0-SSC
@inproceedings{Yang18-DR-L0SSC,
  author    = {Yingzhen Yang},
  title     = {Dimensionality Reduced {\textdollar}{\textbackslash}ell{\^{}}\{0\}{\textdollar}-Sparse
               Subspace Clustering},
  booktitle = {International Conference on Artificial Intelligence and Statistics,
               {AISTATS} 2018, 9-11 April 2018, Playa Blanca, Lanzarote, Canary Islands,
               Spain},
  pages     = {2065--2074},
  year      = {2018},
}

@inproceedings{YangFJYH16-L0SSC,
  author    = {Yingzhen Yang and
               Jiashi Feng and
               Nebojsa Jojic and
               Jianchao Yang and
               Thomas S. Huang},
  title     = {L0-Sparse Subspace Clustering},
  booktitle = {Computer Vision - {ECCV} 2016 - Proceedings of the 14th European Conference on Computer Vision (ECCV), Part {II}},
  address   = {Amsterdam,
               The Netherlands},
  pages     = {731--747},
  year      = {2016},
}

@article{YangFJYH16-L0SSC-ijcv,
  author    = {Yingzhen Yang and
               Jiashi Feng and
               Nebojsa Jojic and
               Jianchao Yang and
               Thomas S. Huang},
  title     = {Subspace Learning by $\ell^0$-Induced Sparsity},
  journal   = {Int. J. Comput. Vis.},
  volume    = {126},
  number    = {10},
  pages     = {1138--1156},
  year      = {2018},
}

@inproceedings{Wang2015-dr-l1ssc,
  author    = {Yining Wang and
               Yu{-}Xiang Wang and
               Aarti Singh},
  title     = {A Deterministic Analysis of Noisy Sparse Subspace Clustering for Dimensionality-reduced
               Data},
  booktitle = {Proceedings of the 32nd International Conference on Machine Learning (ICML)},
  address   = {Lille, France},
  pages     = {1422--1431},
  year      = {2015}
}


%deep SSC
@inproceedings{Peng2016-deep-ssc,
  author    = {Xi Peng and
               Shijie Xiao and
               Jiashi Feng and
               Wei{-}Yun Yau and
               Zhang Yi},
  title     = {Deep Subspace Clustering with Sparsity Prior},
  booktitle = {Proceedings of the 25 International Joint Conference on
               Artificial Intelligence},
  address   = {New York, NY, USA},
  month     = {9-15 July},
  pages     = {1925--1931},
  year      = {2016},
}

@inproceedings{ParkCS14,
  author    = {Dohyung Park and
               Constantine Caramanis and
               Sujay Sanghavi},
  title     = {Greedy Subspace Clustering},
  booktitle = {Advances in Neural Information Processing Systems (NIPS)},
  address   = {Montreal, Canada},
  pages     = {2753--2761},
  year      = {2014}
}

@ARTICLE{Li2018-ASSC,
  author    = {Chun{-}Guang Li and
               Chong You and
               Ren{\'{e}} Vidal},
  title     = {On Geometric Analysis of Affine Sparse Subspace Clustering},
  journal   = {{IEEE} J. Sel. Top. Signal Process.},
  volume    = {12},
  number    = {6},
  pages     = {1520--1533},
  year      = {2018}
}


@inproceedings{YouLRV19-ASSC,
  author    = {Chong You and
               Chun{-}Guang Li and
               Daniel P. Robinson and
               Ren{\'{e}} Vidal},
  title     = {Is an Affine Constraint Needed for Affine Subspace Clustering?},
  booktitle = {Proceedings of the 2019 {IEEE/CVF} International Conference on Computer Vision (ICCV)},
  address   = {Seoul, Korea},
  pages     = {9914--9923},
  publisher = {{IEEE}},
  year      = {2019},
}

@inproceedings{LiuLY10,
  author    = {Guangcan Liu and
               Zhouchen Lin and
               Yong Yu},
  title     = {Robust Subspace Segmentation by Low-Rank Representation},
  booktitle = {Proceedings of the 27th International Conference on Machine Learning (ICML)},
  address   = {Haifa, Israel},
  pages     = {663--670},
  year      = {2010}
}

@ARTICLE{LiuLY13,
  author    = {Guangcan Liu and
               Zhouchen Lin and
               Shuicheng Yan and
               Ju Sun and
               Yong Yu and
               Yi Ma},
  title     = {Robust Recovery of Subspace Structures by Low-Rank Representation},
  journal   = {{IEEE} Trans. Pattern Anal. Mach. Intell.},
  volume    = {35},
  number    = {1},
  pages     = {171--184},
  year      = {2013}
}

@inproceedings{Peng2015robust,
title={Robust Subspace Clustering via Thresholding Ridge Regression},
author={Xi Peng and Zhang Yi and Huajin Tang},
booktitle={AAAI Conference on Artificial Intelligence (AAAI)},
pages={3827--3833},
year={2015},
organization={AAAI} }

@INPROCEEDINGS{Nasihatkon11,
author={Nasihatkon, B. and Hartley, R.},
booktitle={Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on},
title={Graph connectivity in sparse subspace clustering},
year={2011},
month={June},
pages={2137-2144},
keywords={graph theory;image segmentation;minimisation;pattern clustering;2-dimensional subspace;3-dimensional subspace;L1-norm minimization;L1-sparse representation;convex polytope geometry;graph connectivity;sparse subspace clustering;subspace segmentation;Communities;Geometry;Image segmentation;Minimization;Motion segmentation;Three dimensional displays;Vectors},
}

%My sparse graph
@inproceedings{YYZRl1graphBMVC2014,
	title = {Regularized l1-Graph for Data Clustering},
	author = {Yang, Yingzhen and Wang, Zhangyang and Yang, Jianchao and Han, Jiawei and Huang, Thomas},
	year = {2014},
	booktitle = {Proceedings of the British Machine Vision Conference},
	publisher = {BMVA Press},
	editors = {Valstar, Michel and French, Andrew and Pridmore, Tony}
}

@inproceedings{YangWYWCH14,
  author    = {Yingzhen Yang and
               Zhangyang Wang and
               Jianchao Yang and
               Jiangping Wang and
               Shiyu Chang and
               Thomas S. Huang},
  title     = {Data Clustering by Laplacian Regularized L1-Graph},
  booktitle = {Proceedings of the Twenty-Eighth {AAAI} Conference on Artificial Intelligence,
               July 27 -31, 2014, Qu{\'{e}}bec City, Qu{\'{e}}bec, Canada.},
  pages     = {3148--3149},
  year      = {2014},
}


%similarity and graph based clustering

@article{Schaeffer2007,
 author = {Schaeffer, Satu Elisa},
 title = {Survey: Graph Clustering},
 journal = {Comput. Sci. Rev.},
 issue_date = {August, 2007},
 volume = {1},
 number = {1},
 month = aug,
 year = {2007},
 pages = {27--64},
 numpages = {38},
 acmid = {2296057},
 publisher = {Elsevier Science Publishers B. V.},
 address = {Amsterdam, The Netherlands, The Netherlands},
}

@ARTICLE{Frey07clusteringby,
    author = {Brendan J. Frey and Delbert Dueck},
    title = {Clustering by passing messages between data points},
    journal = {Science},
    year = {2007},
    volume = {315},
    pages = {2007}
}


@inproceedings{YangCLH12,
  author    = {Yingzhen Yang and
               Xinqi Chu and
               Feng Liang and
               Thomas S. Huang},
  title     = {Pairwise Exemplar Clustering},
  booktitle = {Proceedings of the Twenty-Sixth {AAAI} Conference on Artificial Intelligence,
               July 22-26, 2012, Toronto, Ontario, Canada.},
  year      = {2012},
}

%l1 as relaxation of l0
@inproceedings{jenatton2010proximal,
  title={Proximal methods for sparse hierarchical dictionary learning},
  author={Jenatton, Rodolphe and Mairal, Julien and Bach, Francis R and Obozinski, Guillaume R},
  booktitle={Proceedings of the 27th International Conference on Machine Learning (ICML-10)},
  pages={487--494},
  year={2010}
}

@article{Mairal2010,
 author = {Mairal, Julien and Bach, Francis and Ponce, Jean and Sapiro, Guillermo},
 title = {Online Learning for Matrix Factorization and Sparse Coding},
 journal = {J. Mach. Learn. Res.},
 issue_date = {3/1/2010},
 volume = {11},
 month = mar,
 year = {2010},
 pages = {19--60},
 numpages = {42},
 acmid = {1756008},
 publisher = {JMLR.org},
}

@inproceedings{MairalBPSZ08,
  author    = {Julien Mairal and
               Francis R. Bach and
               Jean Ponce and
               Guillermo Sapiro and
               Andrew Zisserman},
  title     = {Supervised Dictionary Learning},
  booktitle = {Advances in Neural Information Processing Systems 21, Proceedings
               of the Twenty-Second Annual Conference on Neural Information Processing
               Systems, Vancouver, British Columbia, Canada, December 8-11, 2008},
  pages     = {1033--1040},
  year      = {2008},
}

@INPROCEEDINGS{Mancera2006,
author={Mancera, L. and Portilla, J.},
booktitle={Image Processing, 2006 IEEE International Conference on},
title={L0-Norm-Based Sparse Representation Through Alternate Projections},
year={2006},
month={Oct},
pages={2089-2092},
keywords={image representation;L0-norm;sparse representation;Artificial intelligence;Compaction;Computer science;Degradation;Dictionaries;Equations;Information processing;Minimization methods;Robustness;Vectors;Image representation;restoration},
}

%semi-supervised leanring
@inproceedings{ZhuGL03,
  author    = {Xiaojin Zhu and
               Zoubin Ghahramani and
               John D. Lafferty},
  title     = {Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions},
  booktitle = {Machine Learning, Proceedings of the Twentieth International Conference
               {(ICML} 2003), August 21-24, 2003, Washington, DC, {USA}},
  year      = {2003},
  pages     = {912--919},
}

@inproceedings{ZhouBLWS03,
  author    = {Dengyong Zhou and
               Olivier Bousquet and
               Thomas Navin Lal and
               Jason Weston and
               Bernhard Sch{\"{o}}lkopf},
  title     = {Learning with Local and Global Consistency},
  booktitle = {Advances in Neural Information Processing Systems 16 [Neural Information
               Processing Systems, {NIPS} 2003, December 8-13, 2003, Vancouver and
               Whistler, British Columbia, Canada},
  year      = {2003},
}
@inproceedings{KarasuyamaM13,
  author    = {Masayuki Karasuyama and
               Hiroshi Mamitsuka},
  title     = {Manifold-based Similarity Adaptation for Label Propagation},
  booktitle = {Advances in Neural Information Processing Systems 26: 27th Annual
               Conference on Neural Information Processing Systems 2013. Proceedings
               of a meeting held December 5-8, 2013, Lake Tahoe, Nevada, United States.},
  pages     = {1547--1555},
  year      = {2013},
}


%RIP
@ARTICLE{CandesTao05,
author={Candes, E.J. and Tao, T.},
journal={Information Theory, IEEE Transactions on},
title={Decoding by linear programming},
year={2005},
volume={51},
number={12},
pages={4203-4215},
keywords={Gaussian processes;convex programming;decoding;error correction codes;indeterminancy;linear codes;linear programming;minimisation;random codes;sparse matrices;Gaussian random matrix;basis pursuit;linear code decoding;linear programming;minimization problem;natural error correcting problem;simple convex optimization problem;sparse solution;uncertainty principle;Decoding;Equations;Error correction;Error correction codes;Information theory;Linear code;Linear programming;Mathematics;Sparse matrices;Vectors;Basis pursuit;Gaussian random matrices;decoding of (random) linear codes;duality in optimization;linear codes;linear programming;principal angles;restricted orthonormality;singular values of random matrices;sparse solutions to underdetermined systems},
month={Dec},}

@ARTICLE{CandesPlan11-RIPless,
author={Candes, E.J. and Plan, Y.},
journal={Information Theory, IEEE Transactions on},
title={A Probabilistic and RIPless Theory of Compressed Sensing},
year={2011},
volume={57},
number={11},
pages={7235-7254},
keywords={Fourier analysis;data compression;random processes;signal reconstruction;statistical distributions;Fourier coefficients;Gaussian model;RIPless theory;compressed sensing;frequency measurements;probabilistic theory;probability distribution;restricted isometry property;signal random model;sparse signals;Coherence;Compressed sensing;Convolution;Discrete Fourier transforms;Upper bound;(weak) restricted isometries; $ell_1$ minimization;Compressed sensing;Dantzig selector;Gross' golfing scheme;LASSO;operator Bernstein inequalities;random matrices;sparse regression},
month={Nov},
}

@article{Candes2008,
title = "The restricted isometry property and its implications for compressed sensing ",
journal = "Comptes Rendus Mathematique ",
volume = "346",
number = "9–10",
pages = "589 - 592",
year = "2008",
note = "",
author = "Emmanuel J. Candès",
abstract = "It is now well-known that one can reconstruct sparse or compressible signals accurately from a very limited number of measurements, possibly contaminated with noise. This technique known as “compressed sensing” or “compressive sampling” relies on properties of the sensing matrix such as the restricted isometry property. In this Note, we establish new results about the accuracy of the reconstruction from undersampled measurements which improve on earlier estimates, and have the advantage of being more elegant. To cite this article: E.J. Candès, C. R. Acad. Sci. Paris, Ser. I 346 (2008). ",
}

%optimization properties
@article{zhang2012,
author = "Zhang, Cun-Hui and Zhang, Tong",
fjournal = "Statistical Science",
journal = "Statist. Sci.",
month = "11",
number = "4",
volume = "27",
year = "2012",
pages = "576--593",
publisher = "The Institute of Mathematical Statistics",
title = "A General Theory of Concave Regularization for High-Dimensional Sparse Estimation Problems",
}

%matrix algebra

%perturbation
@Article{Wedin1973-perturbation-pseudo-inverses,
author="Wedin, Per-{\AA}ke",
title="Perturbation theory for pseudo-inverses",
journal="BIT Numerical Mathematics",
year="1973",
volume="13",
number="2",
pages="217--232",
abstract="A perturbation theory for pseudo-inverses is developed. The theory is based on a useful decomposition (theorem 2.1) ofB+ -A+ whereB andA arem {\texttimes}n matrices. Sharp estimates of ∥B+ -A+∥ are derived for unitary invariant norms whenA andB are of the same rank and ∥B -A∥ is small. Under similar conditions the perturbation of a linear systemAx=b is studied. Realistic bounds on the perturbation ofx=A+b andr=b=Ax are given. Finally it is seen thatA+ andB+ can be compared if and only ifR(A) andR(B) as well asR(A                  H                ) andR(B                  H                ) are in the acute case. Some theorems valid only in the acute case are also proved.",
}

@article{Meng2010-perturbation-pseudo-inverses,
title = "The optimal perturbation bounds of the Moore–Penrose inverse under the Frobenius norm ",
journal = "Linear Algebra and its Applications ",
volume = "432",
number = "4",
pages = "956 - 963",
year = "2010",
note = "",
author = "Lingsheng Meng and Bing Zheng",
keywords = "Singular Value Decomposition",
keywords = "Frobenius norm",
keywords = "Moore–Penrose inverse",
keywords = "Additive perturbation",
keywords = "Multiplicative perturbation ",
abstract = "We obtain the optimal perturbation bounds of the Moore–Penrose inverse under the Frobenius norm by using Singular Value Decomposition, which improved the results in the earlier paper [P.-Å. Wedin, Perturbation theory for pseudo-inverses, \{BIT\} 13 (1973) 217–232]. In addition, a perturbation bound of the Moore–Penrose inverse under the Frobenius norm in the case of the multiplicative perturbation model is also given. "
}

@article{Stewart1991-perturbation-singular-value,
  added-at = {2010-04-12T22:27:25.000+0200},
  author = {Stewart, G. W.},
  biburl = {http://www.bibsonomy.org/bibtex/264969df70ef1c4653af63cafd2e99ce9/ytyoun},
  interhash = {8aa16a26eb47afee9e905f60a2c321f5},
  intrahash = {64969df70ef1c4653af63cafd2e99ce9},
  journal = {SVD and Signal Processing, II: Algorithms, Analysis and Applications},
  keywords = {imported singular_value_decomposition},
  pages = {99--109},
  publisher = {Citeseer},
  timestamp = {2012-08-22T15:16:07.000+0200},
  title = {{Perturbation theory for the singular value decomposition}},
  year = 1991
}

%random matrix decomposition
@article{Halko2011-random-matrix-decomposition,
 author = {Halko, N. and Martinsson, P. G. and Tropp, J. A.},
 title = {Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions},
 journal = {SIAM Rev.},
 issue_date = {May 2011},
 volume = {53},
 number = {2},
 month = may,
 year = {2011},
 issn = {0036-1445},
 pages = {217--288},
 numpages = {72},
 acmid = {2078881},
 publisher = {Society for Industrial and Applied Mathematics},
 address = {Philadelphia, PA, USA},
 keywords = {Johnson-Lindenstrauss lemma, dimension reduction, eigenvalue decomposition, interpolative decomposition, matrix approximation, parallel algorithm, pass-efficient algorithm, principal component analysis, random matrix, randomized algorithm, rank-revealing QR factorization, singular value decomposition, streaming algorithm},
}

%NP-hardness of L0
@article{Natarajan95-sparse-linear-system,
author = {B. K. Natarajan},
title = {Sparse Approximate Solutions to Linear Systems},
journal = {SIAM Journal on Computing},
volume = {24},
number = {2},
pages = {227-234},
year = {1995},
}

%randomized optimization
@Article{Drineas2004-large-graph-svd,
  author    = {Petros Drineas and
               Alan M. Frieze and
               Ravi Kannan and
               Santosh S. Vempala and
               V. Vinay},
  title     = {Clustering Large Graphs via the Singular Value Decomposition},
  journal   = {Mach. Learn.},
  volume    = {56},
  number    = {1-3},
  pages     = {9--33},
  year      = {2004}
}

@article{Frieze2004-fast-monto-carlo-lowrank,
 author = {Frieze, Alan and Kannan, Ravi and Vempala, Santosh},
 title = {Fast Monte-carlo Algorithms for Finding Low-rank Approximations},
 journal = {J. ACM},
 issue_date = {November 2004},
 volume = {51},
 number = {6},
 month = nov,
 year = {2004},
 pages = {1025--1041},
 numpages = {17},
 acmid = {1039494},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {Matrix algorithms, low-rank approximation, sampling},
}

@INPROCEEDINGS{Sarlos2006-large-matrix-random-projection,
  author    = {Tam{\'{a}}s Sarl{\'{o}}s},
  title     = {Improved Approximation Algorithms for Large Matrices via Random Projections},
  booktitle = {Proceedings of the 47th Annual {IEEE} Symposium on Foundations of Computer Science (FOCS)},
  address   = {Berkeley, CA},
  pages     = {143--152},
  year      = {2006}
}


@article{Drineas2006-fast-monto-carlo-lowrank,
  author    = {Petros Drineas and
               Ravi Kannan and
               Michael W. Mahoney},
  title     = {Fast Monte Carlo Algorithms for Matrices {II:} Computing a Low-Rank
               Approximation to a Matrix},
  journal   = {{SIAM} J. Comput.},
  volume    = {36},
  number    = {1},
  pages     = {158--183},
  year      = {2006}
}


@article{Drineas2008-matrix-decomposition,
  author    = {Petros Drineas and
               Michael W. Mahoney and
               S. Muthukrishnan},
  title     = {Relative-Error {CUR} Matrix Decompositions},
  journal   = {{SIAM} J. Matrix Anal. Appl.},
  volume    = {30},
  number    = {2},
  pages     = {844--881},
  year      = {2008}
}


@article{Mahoney2009-matrix-decomposition,
author = {Mahoney, Michael W. and Drineas, Petros},
title = {{CUR} matrix decompositions for improved data analysis},
volume = {106},
number = {3},
pages = {697-702},
year = {2009},
abstract ={Principal components analysis and, more generally, the Singular Value Decomposition are fundamental data analysis tools that express a data matrix in terms of a sequence of orthogonal or uncorrelated vectors of decreasing importance. Unfortunately, being linear combinations of up to all the data points, these vectors are notoriously difficult to interpret in terms of the data and processes generating the data. In this article, we develop CUR matrix decompositions for improved data analysis. CUR decompositions are low-rank matrix decompositions that are explicitly expressed in terms of a small number of actual columns and/or actual rows of the data matrix. Because they are constructed from actual data elements, CUR decompositions are interpretable by practitioners of the field from which the data are drawn (to the extent that the original data are). We present an algorithm that preferentially chooses columns and rows that exhibit high “statistical leverage” and, thus, in a very precise statistical sense, exert a disproportionately large “influence” on the best low-rank fit of the data matrix. By selecting columns and rows in this manner, we obtain improved relative-error and constant-factor approximation guarantees in worst-case analysis, as opposed to the much coarser additive-error guarantees of prior work. In addition, since the construction involves computing quantities with a natural and widely studied statistical interpretation, we can leverage ideas from diagnostic regression analysis to employ these matrix decompositions for exploratory data analysis.},
journal = {Proceedings of the National Academy of Sciences}
}

@Article{Drineas2011-fast-least-square-approximation,
  author    = {Petros Drineas and
               Michael W. Mahoney and
               S. Muthukrishnan and
               Tam{\'{a}}s Sarl{\'{o}}s},
  title     = {Faster least squares approximation},
  journal   = {Numerische Mathematik},
  volume    = {117},
  number    = {2},
  pages     = {219--249},
  year      = {2011}
}

@inproceedings{Lu2013-fast-ridge-regression-random-subsample,
  author    = {Yichao Lu and
               Paramveer S. Dhillon and
               Dean P. Foster and
               Lyle H. Ungar},
  title     = {Faster Ridge Regression via the Subsampled Randomized Hadamard Transform},
  booktitle = {Advances in Neural Information Processing Systems (NIPS)},
  address   = {Lake Tahoe, NV},
  pages     = {369--377},
  year      = {2013}
}


@inproceedings{ZhangWZ2016-sparse-linear-random-projection,
author = {Weizhong Zhang and Lijun Zhang and Rong Jin and Deng Cai and Xiaofei He},
title = {Accelerated Sparse Linear Regression via Random Projection},
booktitle = {Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI)},
year = {2016},
pages = {2337--2343},
}

@inproceedings{Zhang2016-sparse-random-convex-concave,
  author    = {Lijun Zhang and
               Tianbao Yang and
               Rong Jin and
               Zhi{-}Hua Zhou},
  title     = {Sparse Learning for Large-Scale and High-Dimensional Data: {A} Randomized
               Convex-Concave Optimization Approach},
  booktitle = {Algorithmic Learning Theory - 27th International Conference, {ALT}
               2016, Bari, Italy, October 19-21, 2016, Proceedings},
  pages     = {83--97},
  year      = {2016},
}

@article{Frankl1987-JL-Lemma,
 author = {Frankl, P. and Maehara, H.},
 title = {The Johnson-Lindenstrauss Lemma and the Sphericity of Some Graphs},
 journal = {J. Comb. Theory Ser. A},
 issue_date = {June 1987},
 volume = {44},
 number = {3},
 month = jun,
 year = {1987},
 pages = {355--362},
 numpages = {8},
 acmid = {48193},
 publisher = {Academic Press, Inc.},
 address = {Orlando, FL, USA},
}

@inproceedings{Indyk1998-ANN,
 author = {Indyk, Piotr and Motwani, Rajeev},
 title = {Approximate Nearest Neighbors: Towards Removing the Curse of Dimensionality},
 booktitle = {Proceedings of the Thirtieth Annual ACM Symposium on Theory of Computing},
 series = {STOC '98},
 year = {1998},
 isbn = {0-89791-962-9},
 location = {Dallas, Texas, USA},
 pages = {604--613},
 numpages = {10},
 acmid = {276876},
 publisher = {ACM},
 address = {New York, NY, USA},
}

@article{Dasgupta2003-JL-proof,
 author = {Dasgupta, Sanjoy and Gupta, Anupam},
 title = {An Elementary Proof of a Theorem of Johnson and Lindenstrauss},
 journal = {Random Struct. Algorithms},
 issue_date = {January 2003},
 volume = {22},
 number = {1},
 month = jan,
 year = {2003},
 pages = {60--65},
 numpages = {6},
 acmid = {639795},
 publisher = {John Wiley \& Sons, Inc.},
 address = {New York, NY, USA},
}

@Article{Arriaga2006,
author="Arriaga, Rosa I.
and Vempala, Santosh",
title="An algorithmic theory of learning: Robust concepts and random projection",
journal="Machine Learning",
year="2006",
volume="63",
number="2",
pages="161--182",
abstract="We study the phenomenon of cognitive learning from an algorithmic standpoint. How does the brain effectively learn concepts from a small number of examples despite the fact that each example contains a huge amount of information? We provide a novel algorithmic analysis via a model of robust concept learning (closely related to ``margin classifiers''), and show that a relatively small number of examples are sufficient to learn rich concept classes. The new algorithms have several advantages---they are faster, conceptually simpler, and resistant to low levels of noise. For example, a robust half-space can be learned in linear time using only a constant number of training examples, regardless of the number of attributes. A general (algorithmic) consequence of the model, that ``more robust concepts are easier to learn'', is supported by a multitude of psychological studies.",
}

@article{Achlioptas2003-database-friendly-random-projection,
 author = {Achlioptas, Dimitris},
 title = {Database-friendly Random Projections: Johnson-Lindenstrauss with Binary Coins},
 journal = {J. Comput. Syst. Sci.},
 issue_date = {1 June 2003},
 volume = {66},
 number = {4},
 month = jun,
 year = {2003},
 pages = {671--687},
 numpages = {17},
 acmid = {861189},
 publisher = {Academic Press, Inc.},
 address = {Orlando, FL, USA},
}

%matrix perturbation
@article{Weyl1912-perturbation-singular-value,
author = {Weyl, H.},
journal = {Mathematische Annalen},
pages = {441-479},
title = {Das asymptotische Verteilungsgesetz der Eigenwerte linearer partieller Differentialgleichungen (mit einer Anwendung auf die Theorie der Hohlraumstrahlung)},
volume = {71},
year = {1912},
}

@Article{Chen2016-perturbation-orthogonal-projection,
  author    = {Yanmei Chen and
               Xiao Shan Chen and
               Wen Li},
  title     = {On perturbation bounds for orthogonal projections},
  journal   = {Numer. Algorithms},
  volume    = {73},
  number    = {2},
  pages     = {433--444},
  year      = {2016}
}

@article{Stewart1977-perturbation-pseudoinverse-projection,
 author = {G. W. Stewart},
 journal = {SIAM Rev.},
 number = {4},
 pages = {634-662},
 publisher = {Society for Industrial and Applied Mathematics},
 title = {On the Perturbation of Pseudo-Inverses, Projections and Linear Least Squares Problems},
 volume = {19},
 year = {1977}
}

%spectrum for random matrix
@incollection{Davidson-local-operator-random-matrix,
    author = {Davidson, K. and Szarek, S.},
    booktitle = {Handbook on the Geometry of Banach spaces},
    citeulike-article-id = {3054664},
    editor = {Lindenstrauss},
    pages = {317--366},
    posted-at = {2008-07-28 21:02:13},
    priority = {2},
    publisher = {Elsevier Science},
    title = {Local operator theory, random matrices and {B}anach spaces},
    volume = {1},
    year = {2001}
}

@article{yaskov2015-least-singular-value-random,
  title={Sharp lower bounds on the least singular value of a random matrix without the fourth moment condition},
  author={Yaskov, Pavel},
  journal={Electronic Communications in Probability},
  volume={20},
  pages={1--9},
  year={2015}
 }


@article{yaskov2014-least-singular-value-random,
author = "Yaskov, Pavel",
fjournal = "Electronic Communications in Probability",
journal = "Electron. Commun. Probab.",
pages = "10 pp.",
pno = "83",
publisher = "The Institute of Mathematical Statistics and the Bernoulli Society",
title = "Lower bounds on the smallest eigenvalue of a sample covariance matrix.",
volume = "19",
year = "2014"
}

@book{aubrun2017alice,
  title={Alice and Bob Meet Banach: The Interface of Asymptotic Geometric Analysis and Quantum Information Theory},
  author={Aubrun, G. and Szarek, S.J.},
  lccn={2017010894},
  series={Mathematical Surveys and Monographs},
  year={2017},
  publisher={American Mathematical Society}
}

@book{vershynin2018-highdim-statistics,
place={Cambridge},
series={Cambridge Series in Statistical and Probabilistic Mathematics}, title={High-Dimensional Probability: An Introduction with Applications in Data Science},
publisher={Cambridge University Press},
author={Vershynin, Roman},
year={2018},
collection={Cambridge Series in Statistical and Probabilistic Mathematics}
}

@inproceedings{YangY19-fast-pgd-l0,
  author    = {Yingzhen Yang and
               Jiahui Yu},
  title     = {Fast Proximal Gradient Descent for {A} Class of Non-convex and Non-smooth
               Sparse Learning Problems},
  booktitle = {Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial
               Intelligence (UAI)},
  address   = {Tel Aviv, Israel},
  pages     = {1253--1262},
  year      = {2019},
}

@ARTICLE{Yang2017-PGD-l0-sparse-approximation,
  title={On the Suboptimality of Proximal Gradient Descent for $\ell^0$ Sparse Approximation},
  author={Yang, Yingzhen and Feng, Jiashi and Jojic, Nebojsa and Yang, Jianchao and Huang, Thomas S},
  journal={arXiv preprint arXiv:1709.01230},
  year={2017}
}

@inproceedings{NelsonN13-OSNAP,
  author    = {Jelani Nelson and
               Huy L. Nguyen},
  title     = {{OSNAP:} Faster Numerical Linear Algebra Algorithms via Sparser Subspace
               Embeddings},
  booktitle = {Proceedings of the 54th Annual {IEEE} Symposium on Foundations of Computer Science (FOCS)},
  address   = {Berkeley, CA},
  pages     = {117--126},
  year      = {2013},
}

@inproceedings{Yang18-DRL0SSC,
  author    = {Yingzhen Yang},
  title     = {Dimensionality Reduced $\ell^0$-Sparse
               Subspace Clustering},
  booktitle = {Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS)},
  address   = {Playa Blanca, Lanzarote, Canary Islands,
               Spain},
  pages     = {2065--2074},
  year      = {2018},
}

%concentration_chi_square
@article{Laurent2000-chi-square-concentration,
author = {B. Laurent and P. Massart},
title = {{Adaptive estimation of a quadratic functional by model selection}},
volume = {28},
journal = {The Annals of Statistics},
number = {5},
publisher = {Institute of Mathematical Statistics},
pages = {1302 -- 1338},
keywords = {$l_p$-bodies, adaptive estimation, Besov bodies, efficient estimation, Gaussian sequence model, Model selection, quadratic functionals},
year = {2000},
}


@article{liu2016low,
  author    = {Guangcan Liu and
               Ping Li},
  title     = {Low-Rank Matrix Completion in the Presence of High Coherence},
  journal   = {{IEEE} Trans. Signal Process.},
  volume    = {64},
  number    = {21},
  pages     = {5623--5633},
  year      = {2016}
}

@inproceedings{liu2014recovery,
  author    = {Guangcan Liu and
               Ping Li},
  title     = {Recovery of Coherent Data via Low-Rank Dictionary Pursuit},
  booktitle = {Advances in Neural Information Processing Systems (NIPS)},
  address   = {
               Montreal,  Canada},
  pages     = {1206--1214},
  year      = {2014}
}

@inproceedings{yuan2014sparse,
  author    = {Xiao{-}Tong Yuan and
               Ping Li},
  title     = {Sparse Additive Subspace Clustering},
  booktitle = {Proceedings of the 13th European Conference on Computer Vision (ECCV), Part {III}},
  address   = {Zurich,
               Switzerland},
  pages     = {644--659},
  year      = {2014}
}

@inproceedings{li2007very,
  author    = {Ping Li},
  title     = {Very sparse stable random projections for dimension reduction in $l\alpha$ ($0\le\alpha\leq2$) norm},
  booktitle = {Proceedings of the 13th {ACM} {SIGKDD} International Conference on
               Knowledge Discovery and Data Mining (KDD)}, 
  address    = {San Jose, CA},
  pages     = {440--449},
  year      = {2007}
}

@inproceedings{li2019sign,
  author    = {Ping Li},
  title     = {Sign-Full Random Projections},
  booktitle = {Proceedings of the Thirty-Third {AAAI} Conference on Artificial Intelligence (AAAI)},
  address   = {Honolulu, HI},
  pages     = {4205--4212},
  year      = {2019}
}

@inproceedings{li2019random,
  author    = {Xiaoyun Li and
               Ping Li},
  title     = {Random Projections with Asymmetric Quantization},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  address   = {Vancouver,  Canada},
  pages     = {10857--10866},
  year      = {2019}
}

@inproceedings{li2017simple,
  author    = {Ping Li},
  title     = {Binary and Multi-Bit Coding for Stable Random Projections},
  booktitle = {Proceedings of the 20th International Conference on Artificial Intelligence
               and Statistics (AISTATS)},
  address   = {Fort Lauderdale, FL},
  pages     = {1430--1438},
  year      = {2017}
}

@inproceedings{li2015compressed,
  author    = {Ping Li and
               Cun{-}Hui Zhang},
  title     = {Compressed Sensing with Very Sparse Gaussian Random Projections},
  booktitle = {Proceedings of the Eighteenth International Conference on Artificial
               Intelligence and Statistics (AISTATS)},
  address   = {San Diego, CA},
  year      = {2015}
}


@article{li2022gcwsnet,
  title={GCWSNet: Generalized Consistent Weighted Sampling for Scalable and Accurate Training of Neural Networks},
  author={Li, Ping and Zhao, Weijie},
  journal={arXiv preprint arXiv:2201.02283},
  year={2022}
}

%spectrum for random matrix
@incollection{Davidson08-local-operator-random-matrix,
    author = {Davidson, K. and Szarek, S.},
    booktitle = {Handbook on the Geometry of Banach spaces},
    citeulike-article-id = {3054664},
    editor = {Lindenstrauss},
    pages = {317--366},
    posted-at = {2008-07-28 21:02:13},
    priority = {2},
    publisher = {Elsevier Science},
    title = {Local operator theory, random matrices and {B}anach spaces},
    volume = {1},
    year = {2001}
}

@article{Article:JL84,
  author    = {William B. Johnson and Joram Lindenstrauss},
  title     = {Extensions of \text{Lipschitz} mapping into \text{Hilbert} space},
  journal   = {Contemporary Mathematics},
  volume    = {26},
  year      = {1984},
  pages     = {189-206}
}

@inproceedings{Proc:Dasgupta_UAI00,
  author    = {Sanjoy Dasgupta},
  title     = {Experiments with Random Projection},
  booktitle = {Proceedings of the 16th Conference in Uncertainty in Artificial Intelligence (UAI)},
  address   = {Stanford, CA},
  pages     = {143--151},
  year      = {2000}
}

@inproceedings{Proc:Bingham_KDD01,
  author    = {Ella Bingham and Heikki Mannila},
  title     = {Random projection in dimensionality reduction:
Applications to image and text data},
  booktitle = {Proceedings of the Seventh {ACM} {SIGKDD} International Conference
               on Knowledge Discovery and Data Mining (KDD)},
  year      = {2001},
  address   = {San Francisco, CA },
  pages     = {245-250}
}

@Article{Article:Buher_01,
  author        = "Jeremy Buhler",
  title         = "Efficient large-scale sequence comparison by locality-sensitive hashing",
   journal     = "Bioinformatics",
  volume        = "17",
  number        = "5",
  year          = "2001",
  pages         = "419-428"
}

@article{Article:Achlioptas_JCSS03,
  author    = {Dimitris Achlioptas},
  title     = {Database-friendly random projections: Johnson-Lindenstrauss with binary
               coins},
  journal   = {J. Comput. Syst. Sci.},
  volume    = {66},
  number    = {4},
  pages     = {671--687},
  year      = {2003}
}

@inproceedings{Proc:Fern_ICML03,
  author    = {Xiaoli Zhang Fern and Carla E. Brodley},
  title     = {Random Projection for High Dimensional Data Clustering: A cluster Ensemble Approach},
  booktitle = {Proceedings of the Twentieth International Conference (ICML)},
  year      = {2003},
  address   = {Washington, DC },
  pages     = {186-193}
}

@inproceedings{Proc:Datar_SCG04,
  author        = "Mayur Datar and Nicole Immorlica and Piotr Indyk and Vahab S. Mirrokn",
  title         = "Locality-sensitive hashing scheme based on p-stable distributions",
  booktitle     = "Proceedings of the 20th {ACM} Symposium on Computational Geometr (SCG)",
  address       = "Brooklyn, NY",
  year          = "2004",
  pages         = " 253 - 262"
}

@article{Article:Candes_IT06,
  author    = {Emmanuel J. Cand{\`{e}}s and
               Justin K. Romberg and
               Terence Tao},
  title     = {Robust uncertainty principles: exact signal reconstruction from highly
               incomplete frequency information},
  journal   = {{IEEE} Trans. Inf. Theory},
  volume    = {52},
  number    = {2},
  pages     = {489--509},
  year      = {2006}
}

@article{Article:Donoho_IT06,
  author    = {David L. Donoho},
  title     = {Compressed sensing},
  journal   = {{IEEE} Trans. Inf. Theory},
  volume    = {52},
  number    = {4},
  pages     = {1289--1306},
  year      = {2006}
}

@inproceedings{Proc:Frund_NIPS07,
  author    = {Yoav Freund and
               Sanjoy Dasgupta and
               Mayank Kabra and
               Nakul Verma},
  title     = {Learning the structure of manifolds using random projections},
  booktitle = {Advances in Neural Information Processing Systems (NIPS)},
  year      = {2007},
  pages     = {473--480},  
  address   = {Vancouver, Canada}
}

@article{Article:Charikar_2004,
  author    = {Moses Charikar and
               Kevin Chen and
               Martin Farach-Colton},
  title     = {Finding frequent items in data streams},
  journal   = {Theor. Comput. Sci.},
  volume    = {312},
  number    = {1},
  year      = {2004},
  pages     = {3-15}
}

@article{Article:Cormode_05,
  author        = "Graham Cormode and S. Muthukrishnan",
  title         = "An improved data stream summary: the count-min sketch and its applications",
  journal     = "Journal of Algorithm",
  volume        = "55",
  number        = "1",
  year          = "2005",
  pages         = "58-75"
}

@inproceedings{Proc:Weinberger_ICML2009,
 author = {Weinberger, Kilian and Dasgupta, Anirban and Langford, John and Smola, Alex and Attenberg, Josh},
 title = {Feature hashing for large scale multitask learning},
 booktitle = {ICML},
 year = {2009},
 pages = {1113--1120}
}

@ARTICLE{Article:Gilbert_IEEE10,
author={Gilbert, Anna and Indyk, Piotr.},
journal={Proceedings of the IEEE},
title={Sparse Recovery Using Sparse Matrices},
year={2010},
month={june },
volume={98},
number={6},
pages={937 -947}
}

@inproceedings{Proc:Li_NIPS11_Hashing,
 author = {Ping Li and Anshumali Shrivastava and Joshua Moore and Arnd Christian K\"onig},
 title =        {Hashing Algorithms for Large-Scale Learning},
 booktitle = {Advances in Neural Information Processing Systems (NIPS)},
 year = {2011},
 pages     = {2672--2680},
 address = {Granada, Spain}
}