% 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},
 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},
}

@INPROCEEDINGS{HeZheng11,
author={Ran He and Wei-Shi Zheng and Bao-Gang Hu and Xiang-Wei Kong},
booktitle={Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on},
title={Nonnegative sparse coding for discriminative semi-supervised learning},
year={2011},
pages={2849-2856},
keywords={approximation theory;graph theory;iterative decoding;learning (artificial intelligence);pattern clustering;probability;sparse matrices;SPG;adaptive label propagation algorithm;approximation algorithm;clustering indicators;discriminative graph;discriminative semisupervised learning;graph-based semisupervised learning method;informative graph;l0-l1 equivalence theory;l1-norm sparsity technique;large scale sparse graph learning;nonnegative sparse coding;nonnegative sparse weights algorithm;sparse nonnegative graph learning;sparse probability graph;Clustering algorithms;Databases;Encoding;Machine learning;Prediction algorithms;Sparse matrices;Training},
doi={10.1109/CVPR.2011.5995487},
ISSN={1063-6919},
month={June},}

@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},
  ee        = {http://dx.doi.org/10.1109/TIP.2009.2038764},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}



@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},
  ee        = {http://www.jmlr.org/papers/v7/belkin06a.html},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

@inproceedings{Zheng04,
 author = {Zheng, Xin and Cai, Deng and He, Xiaofei and Ma, Wei-Ying and Lin, Xueyin},
 title = {Locality Preserving Clustering for Image Database},
 booktitle = {Proceedings of the 12th Annual ACM International Conference on Multimedia},
 series = {MULTIMEDIA '04},
 year = {2004},
 location = {New York, NY, USA},
 pages = {885--891},
 numpages = {7},
 acmid = {1027731},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {image clustering, locality preserving clustering, locality preserving projections, spectral clustering},
}

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

@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},
 issn = {1935-8237},
 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.},
ISSN={1041-4347},}

@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 = {NIPS},
  year      = {2001},
  pages     = {849-856},
}

@article{Fraley02,
author = {Chris Fraley and Adrian E Raftery},
title = {Model-Based Clustering, Discriminant Analysis, and Density Estimation},
journal = {Journal of the American Statistical Association},
volume = {97},
number = {458},
pages = {611-631},
year  = {2002},
publisher = {Taylor & Francis},
}

@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},
}

@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.},
ISSN={1041-4347},}

@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},
ISSN={1063-6919},}

@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},
journal={Mathematical Programming},
volume={146},
number={1-2},
title={Proximal alternating linearized minimization for nonconvex and nonsmooth problems},
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},
author={Bolte, Jérôme and Sabach, Shoham and Teboulle, Marc},
pages={459-494},
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},
}

%%SSC
@inproceedings{ElhamifarV11,
  author    = {Ehsan Elhamifar and
               Ren{\'e} Vidal},
  title     = {Sparse Manifold Clustering and Embedding},
  booktitle = {NIPS},
  year      = {2011},
  pages     = {55-63},
}

@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},
}

@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} 2013, Atlanta, GA, USA, 16-21 June 2013},
  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",
volume = "42",
year = "2014",
number = "2",
pages = "669--699",
publisher = "The Institute of Mathematical Statistics",
title = "Robust subspace clustering",
}

%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},
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},
doi={10.1109/ICIP.2006.312819},
ISSN={1522-4880},
month={Oct},}

@inproceedings{YangWYWCH14-laplacian-l1graph,
  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},
}

@inproceedings{Yang2014-rl1graph,
	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}
}

@article{YangFYH15,
  author    = {Yingzhen Yang and
               Jiashi Feng and
               Jianchao Yang and
               Thomas S. Huang},
  title     = {Learning {\textdollar}{\textbackslash}ell{\^{}}\{0\}{\textdollar}-Graph
               for Data Clustering},
  journal   = {CoRR},
  volume    = {abs/1510.08520},
  year      = {2015},
}

%manifold
@ARTICLE{RoweisLLE,
    author = {Sam T. Roweis and Lawrence K. Saul},
    title = {Nonlinear dimensionality reduction by locally linear embedding},
    journal = {SCIENCE},
    year = {2000},
    volume = {290},
    pages = {2323--2326}
}

%data set
@article{GrossMultiPIE,
 author = {Gross, Ralph and Matthews, Iain and Cohn, Jeffrey and Kanade, Takeo and Baker, Simon},
 title = {Multi-PIE},
 journal = {Image Vision Comput.},
 issue_date = {May, 2010},
 volume = {28},
 number = {5},
 month = may,
 year = {2010},
 pages = {807--813},
 numpages = {7},
 acmid = {1747071},
 publisher = {Butterworth-Heinemann},
 address = {Newton, MA, USA},
 keywords = {Face database, Face recognition across expression, Face recognition across illumination, Face recognition across pose},
}

@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"
}

%optimization
@Article{Nesterov2005-nonsmooth-optimization,
author="Nesterov, Yu.",
title="Smooth minimization of non-smooth functions",
journal="Mathematical Programming",
year="2005",
month="May",
day="01",
volume="103",
number="1",
pages="127--152",
abstract="In this paper we propose a new approach for constructing efficient schemes for non-smooth convex optimization. It is based on a special smoothing technique, which can be applied to functions with explicit max-structure. Our approach can be considered as an alternative to black-box minimization. From the viewpoint of efficiency estimates, we manage to improve the traditional bounds on the number of iterations of the gradient schemes from                                    keeping basically the complexity of each iteration unchanged.",
}

@Article{Nesterov2013-gradient-composite,
author="Nesterov, Yu.",
title="Gradient methods for minimizing composite functions",
journal="Mathematical Programming",
year="2013",
month="Aug",
day="01",
volume="140",
number="1",
pages="125--161",
abstract="In this paper we analyze several new methods for solving optimization problems with the objective function formed as a sum of two terms: one is smooth and given by a black-box oracle, and another is a simple general convex function with known structure. Despite the absence of good properties of the sum, such problems, both in convex and nonconvex cases, can be solved with efficiency typical for the first part of the objective. For convex problems of the above structure, we consider primal and dual variants of the gradient method (with convergence rate                                                                           {\$}{\$}O{\backslash}left({\{}1 {\backslash}over k{\}}{\backslash}right){\$}{\$}                ), and an accelerated multistep version with convergence rate                                                                           {\$}{\$}O{\backslash}left({\{}1 {\backslash}over k^2{\}}{\backslash}right){\$}{\$}                , where                                                                           {\$}{\$}k{\$}{\$}                 is the iteration counter. For nonconvex problems with this structure, we prove convergence to a point from which there is no descent direction. In contrast, we show that for general nonsmooth, nonconvex problems, even resolving the question of whether a descent direction exists from a point is NP-hard. For all methods, we suggest some efficient ``line search'' procedures and show that the additional computational work necessary for estimating the unknown problem class parameters can only multiply the complexity of each iteration by a small constant factor. We present also the results of preliminary computational experiments, which confirm the superiority of the accelerated scheme.",
}

@book{Rockafellar-Wets2009-variational-analysis,
  title={Variational Analysis},
  author={R. Tyrrell Rockafellar and Roger J-B Wets},
  year={2009},
  volume={317},
  publisher={Springer Science \& Business Media}
}

@article{GhadimiL16-accelerated-gradient-nonconvex,
  author       = {Saeed Ghadimi and
                  Guanghui Lan},
  title        = {Accelerated gradient methods for nonconvex nonlinear and stochastic
                  programming},
  journal      = {Math. Program.},
  volume       = {156},
  number       = {1-2},
  pages        = {59--99},
  year         = {2016},
}

@inproceedings{VinyalsBLKW16-oneshot-learning,
  author    = {Oriol Vinyals and
               Charles Blundell and
               Tim Lillicrap and
               Koray Kavukcuoglu and
               Daan Wierstra},
  editor    = {Daniel D. Lee and
               Masashi Sugiyama and
               Ulrike von Luxburg and
               Isabelle Guyon and
               Roman Garnett},
  title     = {Matching Networks for One Shot Learning},
  booktitle = {Advances in Neural Information Processing Systems 29: Annual Conference
               on Neural Information Processing Systems 2016, December 5-10, 2016,
               Barcelona, Spain},
  pages     = {3630--3638},
  year      = {2016},
}
