\begin{thebibliography}{33}
\providecommand{\natexlab}[1]{#1}
\providecommand{\url}[1]{\texttt{#1}}
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  \providecommand{\doi}[1]{doi: #1}\else
  \providecommand{\doi}{doi: \begingroup \urlstyle{rm}\Url}\fi

\bibitem[Artstein et~al.(2004)Artstein, Ball, Barthe, and Naor]{artstein2004solution}
Shiri Artstein, Keith Ball, Franck Barthe, and Assaf Naor.
\newblock Solution of shannon’s problem on the monotonicity of entropy.
\newblock \emph{Journal of the American Mathematical Society}, 17\penalty0 (4):\penalty0 975--982, 2004.

\bibitem[Brickley et~al.(2019)Brickley, Burgess, and Noy]{brickley2019google}
Dan Brickley, Matthew Burgess, and Natasha Noy.
\newblock Google dataset search: Building a search engine for datasets in an open web ecosystem.
\newblock In \emph{The World Wide Web Conference}, pages 1365--1375, 2019.

\bibitem[Cai et~al.(2010)Cai, Zhang, and He]{cai2010unsupervised}
Deng Cai, Chiyuan Zhang, and Xiaofei He.
\newblock Unsupervised feature selection for multi-cluster data.
\newblock In \emph{Proceedings of the 16th ACM SIGKDD International conference on Knowledge discovery and data mining}, pages 333--342. ACM, 2010.

\bibitem[Faivishevsky and Goldberger(2012)]{faivishevsky2012unsupervised}
Lev Faivishevsky and Jacob Goldberger.
\newblock Unsupervised feature selection based on non-parametric mutual information.
\newblock In \emph{2012 IEEE International Workshop on Machine Learning for Signal Processing}, pages 1--6, Santander, Spain, 2012. IEEE.

\bibitem[Feng et~al.(2016)Feng, Jiao, Liu, Sun, and Zhang]{feng2016unsupervised}
Jie Feng, Licheng Jiao, Fang Liu, Tao Sun, and Xiangrong Zhang.
\newblock Unsupervised feature selection based on maximum information and minimum redundancy for hyperspectral images.
\newblock \emph{Pattern Recognition}, 51:\penalty0 295--309, 2016.

\bibitem[Han(1978)]{te1978nonnegative}
Te~Sun Han.
\newblock Nonnegative entropy measures of multivariate symmetric correlations.
\newblock \emph{Information and Control}, 36:\penalty0 133--156, 1978.

\bibitem[He et~al.(2006)He, Cai, and Niyogi]{he2006laplacian}
Xiaofei He, Deng Cai, and Partha Niyogi.
\newblock Laplacian score for feature selection.
\newblock In \emph{Advances in neural information processing systems}, pages 507--514, 2006.

\bibitem[Hu et~al.(2022)Hu, Gao, Li, Zhang, and Gao]{hu2022feature}
Liang Hu, Lingbo Gao, Yonghao Li, Ping Zhang, and Wanfu Gao.
\newblock Feature-specific mutual information variation for multi-label feature selection.
\newblock \emph{Information Sciences}, 593:\penalty0 449--471, 2022.

\bibitem[Jensen(1906)]{jensen1906fonctions}
Johan Ludwig William~Valdemar Jensen.
\newblock Sur les fonctions convexes et les in{\'e}galit{\'e}s entre les valeurs moyennes.
\newblock \emph{Acta mathematica}, 30\penalty0 (1):\penalty0 175--193, 1906.

\bibitem[Karami et~al.(2023)Karami, Saberi-Movahed, Tiwari, Marttinen, and Vahdati]{karami2023unsupervised}
Saeed Karami, Farid Saberi-Movahed, Prayag Tiwari, Pekka Marttinen, and Sahar Vahdati.
\newblock Unsupervised feature selection based on variance-covariance subspace distance.
\newblock \emph{Neural Networks}, 2023.

\bibitem[Lee and Kim(2013)]{lee2013feature}
Jaesung Lee and Dae-Won Kim.
\newblock Feature selection for multi-label classification using multivariate mutual information.
\newblock \emph{Pattern Recognition Letters}, 34\penalty0 (3):\penalty0 349--357, 2013.

\bibitem[Lee and Kim(2015)]{lee2015mutual}
Jaesung Lee and Dae-Won Kim.
\newblock Mutual information-based multi-label feature selection using interaction information.
\newblock \emph{Expert Systems with Applications}, 42\penalty0 (4):\penalty0 2013--2025, 2015.

\bibitem[Li et~al.(2012)Li, Yang, Liu, Zhou, Lu, et~al.]{li2012unsupervised}
Zechao Li, Yi~Yang, Jing Liu, Xiaofang Zhou, Hanqing Lu, et~al.
\newblock Unsupervised feature selection using nonnegative spectral analysis.
\newblock In \emph{Proceedings of the 26th Association for the Advancement of Artificial Intelligence}, volume~2, pages 1026--1032, 2012.

\bibitem[Lim and Kim(2021)]{lim2021pairwise}
Hyunki Lim and Dae-Won Kim.
\newblock Pairwise dependence-based unsupervised feature selection.
\newblock \emph{Pattern Recognition}, 111:\penalty0 107663, 2021.

\bibitem[Miao et~al.(2022)Miao, Yang, Sun, Fei, Niu, and Shi]{miao2022graph}
Jianyu Miao, Tiejun Yang, Lijun Sun, Xuan Fei, Lingfeng Niu, and Yong Shi.
\newblock Graph regularized locally linear embedding for unsupervised feature selection.
\newblock \emph{Pattern Recognition}, 122:\penalty0 108299, 2022.

\bibitem[Puerto-Santana et~al.(2023)Puerto-Santana, Larra{\~n}aga, and Bielza]{puerto2023feature}
Carlos Puerto-Santana, Pedro Larra{\~n}aga, and Concha Bielza.
\newblock Feature subset selection in data-stream environments using asymmetric hidden markov models and novelty detection.
\newblock \emph{Neurocomputing}, 554:\penalty0 126641, 2023.

\bibitem[Reiman et~al.(2020)Reiman, Metwally, Sun, and Dai]{reiman2020popphy}
Derek Reiman, Ahmed~A Metwally, Jun Sun, and Yang Dai.
\newblock Popphy-cnn: a phylogenetic tree embedded architecture for convolutional neural networks to predict host phenotype from metagenomic data.
\newblock \emph{IEEE journal of biomedical and health informatics}, 24\penalty0 (10):\penalty0 2993--3001, 2020.

\bibitem[Seo et~al.(2019)Seo, Kim, and Lee]{seo2019generalized}
Wangduk Seo, Dae-Won Kim, and Jaesung Lee.
\newblock Generalized information-theoretic criterion for multi-label feature selection.
\newblock \emph{IEEE Access}, 7:\penalty0 122854--122863, 2019.

\bibitem[Shang et~al.(2022)Shang, Zhang, Lu, Jiao, and Li]{shang2022feature}
Ronghua Shang, Weitong Zhang, Mengyao Lu, Licheng Jiao, and Yangyang Li.
\newblock Feature selection based on non-negative spectral feature learning and adaptive rank constraint.
\newblock \emph{Knowledge-Based Systems}, 236:\penalty0 107749, 2022.

\bibitem[Solorio-Fern{\'a}ndez et~al.(2020)Solorio-Fern{\'a}ndez, Carrasco-Ochoa, and Mart{ \'\i}nez-Trinidad]{solorio2020review}
Sa{\'u}l Solorio-Fern{\'a}ndez, J~Ariel Carrasco-Ochoa, and Jos{\'e}~Fco Mart{ \'\i}nez-Trinidad.
\newblock A review of unsupervised feature selection methods.
\newblock \emph{Artificial Intelligence Review}, 53\penalty0 (2):\penalty0 907--948, 2020.

\bibitem[Talukdar et~al.(2018)Talukdar, Hazarika, and Gan]{talukdar2018kernel}
Upasana Talukdar, Shyamanta~M Hazarika, and John~Q Gan.
\newblock A kernel partial least square based feature selection method.
\newblock \emph{Pattern Recognition}, 83:\penalty0 91--106, 2018.

\bibitem[Villa et~al.(2021)Villa, Narayanan, Van~Huffel, Bertrand, and Varon]{villa2021utility}
Amalia Villa, Abhijith~Mundanad Narayanan, Sabine Van~Huffel, Alexander Bertrand, and Carolina Varon.
\newblock Utility metric for unsupervised feature selection.
\newblock \emph{PeerJ Computer Science}, 7:\penalty0 e477, 2021.

\bibitem[Wang et~al.(2023)Wang, Nie, Wang, Wang, and Li]{wang2023outliers}
Sisi Wang, Feiping Nie, Zheng Wang, Rong Wang, and Xuelong Li.
\newblock Outliers robust unsupervised feature selection for structured sparse subspace.
\newblock \emph{IEEE Transactions on Knowledge and Data Engineering}, 2023.

\bibitem[Wang et~al.(2015)Wang, Tang, and Liu]{wang2015embedded}
Suhang Wang, Jiliang Tang, and Huan Liu.
\newblock Embedded unsupervised feature selection.
\newblock In \emph{Proceedings of the 29th Association for the Advancement of Artificial Intelligence}, pages 470--476, 2015.

\bibitem[Wang et~al.(2022)Wang, Li, and Ruiz]{wang2022feature}
Yadi Wang, Xiaoping Li, and Rub{\'e}n Ruiz.
\newblock Feature selection with maximal relevance and minimal supervised redundancy.
\newblock \emph{IEEE Transactions on Cybernetics}, 53\penalty0 (2):\penalty0 707--717, 2022.

\bibitem[Watanabe(1969)]{watanabe1969knowing}
Satoshi Watanabe.
\newblock Knowing and guessing: A quantitative study of inference and information.
\newblock \emph{Wiley, New York}, 1969.

\bibitem[Yang et~al.(2011)Yang, Shen, Ma, Huang, and Zhou]{yang2011l2}
Yi~Yang, Heng~Tao Shen, Zhigang Ma, Zi~Huang, and Xiaofang Zhou.
\newblock $l_{2,1}$-norm regularized discriminative feature selection for unsupervised learning.
\newblock In \emph{Proceedings of International Joint Conference on Artificial Intelligence}, page 1589, 2011.

\bibitem[Yuan et~al.(2021{\natexlab{a}})Yuan, Chen, Li, Yu, Sang, and Luo]{yuan2021unsupervised}
Zhong Yuan, Hongmei Chen, Tianrui Li, Zeng Yu, Binbin Sang, and Chuan Luo.
\newblock Unsupervised attribute reduction for mixed data based on fuzzy rough sets.
\newblock \emph{Information Sciences}, 572:\penalty0 67--87, 2021{\natexlab{a}}.

\bibitem[Yuan et~al.(2021{\natexlab{b}})Yuan, Chen, Zhang, Wan, and Li]{yuan2021novel}
Zhong Yuan, Hongmei Chen, Pengfei Zhang, Jihong Wan, and Tianrui Li.
\newblock A novel unsupervised approach to heterogeneous feature selection based on fuzzy mutual information.
\newblock \emph{IEEE Transactions on Fuzzy Systems}, 30\penalty0 (9):\penalty0 3395--3409, 2021{\natexlab{b}}.

\bibitem[Zhang et~al.(2023)Zhang, Li, Yuan, Deng, Wang, Wang, and Zhang]{zhang2023possibilistic}
Pengfei Zhang, Tianrui Li, Zhong Yuan, Zhixuan Deng, Guoqiang Wang, Dexian Wang, and Fan Zhang.
\newblock A possibilistic information fusion-based unsupervised feature selection method using information quality measures.
\newblock \emph{IEEE Transactions on Fuzzy Systems}, 2023.

\bibitem[Zhang et~al.(2020)Zhang, Zhang, and Li]{zhang2020unsupervised}
Rui Zhang, Yunxing Zhang, and Xuelong Li.
\newblock Unsupervised feature selection via adaptive graph learning and constraint.
\newblock \emph{IEEE Transactions on Neural Networks and Learning Systems}, 33\penalty0 (3):\penalty0 1355--1362, 2020.

\bibitem[Zhong et~al.(2011)Zhong, Wu, Shi, and Gao]{zhong2011energy}
Jiaofei Zhong, Weili Wu, Yan Shi, and Xiaofeng Gao.
\newblock Energy-efficient tree-based indexing schemes for information retrieval in wireless data broadcast.
\newblock In \emph{International Conference on Database Systems for Advanced Applications}, pages 335--351. Springer, 2011.

\bibitem[Zhu et~al.(2023)Zhu, Hou, Tang, Liu, Zhao, and Wang]{zhu2023unsupervised}
Peican Zhu, Xin Hou, Keke Tang, Yang Liu, Yin-Ping Zhao, and Zhen Wang.
\newblock Unsupervised feature selection through combining graph learning and $l_{2,0}$-norm constraint.
\newblock \emph{Information Sciences}, 622:\penalty0 68--82, 2023.

\end{thebibliography}
