Feature selection based on structured sparsity: a comprehensive study

IEEE Transactions on Neural Networks and Learning Systems

Volumn 28, Issue 7, 2017, Pages 1490-1507

  Gui, Jie ^a   Sun, Zhenan ^b   Ji, Shuiwang ^c   Tao, Dacheng ^d   Tan, Tieniu ^b  

^a INSTITUTE OF INTELLIGENT MACHINES   (China)

^b INSTITUTE OF AUTOMATION   (China)

^c Washington State University   (United States)

^d UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

Author keywords

Dimensionality reduction; feature selection; sparse; structured sparsity

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLUSTERING ALGORITHMS; DATA MINING; LEARNING ALGORITHMS; LEARNING SYSTEMS; PATTERN RECOGNITION;

HIGH DIMENSIONAL DATA; MATHEMATICAL REPRESENTATIONS; MULTI-LABEL LEARNING; MULTI-VIEW LEARNING; MULTITASK LEARNING; REGULARIZATION STRATEGIES; RELEVANT FEATURES; STRUCTURED SPARSITIES;

FEATURE EXTRACTION;

EID: 84964546292     PISSN: 2162237X     EISSN: 21622388     Source Type: Journal    
DOI: 10.1109/TNNLS.2016.2551724     Document Type: Article

References (169)

1. I. Guyon, and A. Elisseeff, "An introduction to variable, and feature selection," J. Mach. Learn. Res., vol. 3, pp. 1157-1182, Jan. 2003
2. H. Liu, and L. Yu, "Toward integrating feature selection algorithms for classification, and clustering," IEEE Trans. Knowl. Data Eng., vol. 17, no. 4, pp. 491-502, Apr. 2005
3. Y. Saeys, I. Inza, and P. Larrañaga, "A review of feature selection techniques in bioinformatics," Bioinformatics, vol. 23, no. 19, pp. 2507-2517, 2007
4. S. Alelyani, J. Tang, and H. Liu, "Feature selection for clustering: A review," in Data Clustering: Algorithms, and Applications, C. C. Aggarwal, and C. K. Reddy, Eds. Boca Raton, FL, USA: CRC Press, 2013
5. J. Tang, S. Alelyani, and H. Liu, "Feature selection for classification: A review," in Data Classification: Algorithms, and Applications, C. C. Aggarwal, Ed. Boca Raton, FL, USA: CRC Press, 2013
6. C. Freeman, D. Kulić, and O. Basir, "An evaluation of classifier-specific filter measure performance for feature selection," Pattern Recognit., vol. 48, no. 5, pp. 1812-1826, 2015
7. A.-C. Haury, P. Gestraud, and J.-P. Vert, "The influence of feature selection methods on accuracy, stability, and interpretability of molecular signatures," PLoS ONE, vol. 6, no. 12, p. e28210, 2011
8. D. Tao, X. Tang, X. Li, and X. Wu, "Asymmetric bagging, and random subspace for support vector machines-based relevance feedback in image retrieval," IEEE Trans. Pattern Anal. Mach. Intell., vol. 28, no. 7, pp. 1088-1099, Jul. 2006
9. P. N. Belhumeur, J. P. Hespanha, and D. Kriegman, "Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection," IEEE Trans. Pattern Anal. Mach. Intell., vol. 19, no. 7, pp. 711-720, Jul. 1997
10. D. Tao, X. Li, X. Wu, and S. J. Maybank, "General tensor discriminant analysis, and Gabor features for gait recognition," IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 10, pp. 1700-1715, Oct. 2007
11. D. Tao, J. Cheng, M. Song, and X. Lin, "Manifold ranking-based matrix factorization for saliency detection," IEEE Trans. Neural Netw. Learn. Syst., to be published
12. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2nd ed. Hoboken, NJ, USA: Wiley, 2001
13. Q. Gu, Z. Li, and J. Han, "Generalized Fisher score for feature selection," in Proc. 27th Conf. Uncertainty Artif. Intell., 2011, pp. 266-273
14. H. Cheng, Z. Liu, L. Yang, and X. Chen, "Sparse representation, and learning in visual recognition: Theory, and applications," Signal Process., vol. 93, no. 6, pp. 1408-1425, 2013
15. M. Elad, Sparse, and Redundant Representations: From Theory to Applications in Signal, and Image Processing. New York, NY, USA: Springer, 2010
16. Y. Kim, and J. Kim, "Gradient Lasso for feature selection," in Proc. 21st Int. Conf. Mach. Learn., 2013, pp. 60-67
17. Y. Cong, S. Wang, J. Liu, J. Cao, Y. Yang, and J. Luo, "Deep sparse feature selection for computer aided endoscopy diagnosis," Pattern Recognit., vol. 48, no. 3, pp. 907-917, 2015
18. H. Yan, and J. Yang, "Sparse discriminative feature selection," Pattern Recognit., vol. 48, no. 5, pp. 1827-1835, 2015
19. X. Zhu, H.-I. Suk, and D. Shen, "Matrix-similarity based loss function, and feature selection for Alzheimer's disease diagnosis," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2014, pp. 3089-3096
20. S. Xiang, X. Shen, and J. Ye, "Efficient nonconvex sparse group feature selection via continuous, and discrete optimization," Artif. Intell., vol. 224, pp. 28-50, Jul. 2015
21. J. Liu, J. Ye, and R. Fujimaki, "Forward-backward greedy algorithms for general convex smooth functions over a cardinality constraint," in Proc. 31st Int. Conf. Mach. Learn., 2014, pp. 503-511
22. N. Naikal, A. Y. Yang, and S. S. Sastry, "Informative feature selection for object recognition via sparse PCA," in Proc. IEEE Int. Conf. Comput. Vis., Nov. 2011, pp. 818-825
23. R. Luss, and A. d'Aspremont, "Clustering, and feature selection using sparse principal component analysis," Optim. Eng., vol. 11, no. 1, pp. 145-157, 2010
24. A. Destrero, C. De Mol, F. Odone, and A. Verri, "A regularized framework for feature selection in face detection, and authentication," Int. J. Comput. Vis., vol. 83, no. 2, pp. 164-177, 2009
25. A. Destrero, C. De Mol, F. Odone, and A. Verri, "A sparsity-enforcing method for learning face features," IEEE Trans. Image Process., vol. 18, no. 1, pp. 188-201, Jan. 2009
26. F. Nie, H. Huang, X. Cai, and C. Ding, "Efficient, and robust feature selection via joint l2,1-norms minimization," in Proc. Adv. Neural Inf. Process. Syst., 2010, pp. 1813-1821
27. J. Gui, Z. Sun, W. Jia, R. Hu, Y. Lei, and S. Ji, "Discriminant sparse neighborhood preserving embedding for face recognition," Pattern Recognit., vol. 45, no. 8, pp. 2884-2893, 2012
28. F. Bach, R. Jenatton, J. Mairal, and G. Obozinski, "Structured sparsity through convex optimization," Statist. Sci., vol. 27, no. 4, pp. 450-468, 2012
29. C. Hegde, P. Indyk, and L. Schmidt, "A nearly-linear time framework for graph-structured sparsity," in Proc. 32nd Int. Conf. Mach. Learn., 2015, pp. 928-937
30. W. Y. Wang, K. Mazaitis, and W.W. Cohen, "A soft version of predicate invention based on structured sparsity," in Proc. 24th Int. Joint Conf. Artif. Intell., 2015, pp. 3918-3924
31. J. Liu, P. Wonka, and J. Ye, "A multi-stage framework for Dantzig selector, and Lasso," J. Mach. Learn. Res., vol. 13, no. 1, pp. 1189-1219, 2012
32. J. Wright, Y. Ma, J. Mairal, G. Sapiro, T. S. Huang, and S. Yan, "Sparse representation for computer vision, and pattern recognition," Proc. IEEE, vol. 98, no. 6, pp. 1031-1044, Jun. 2010
33. F. Bach, R. Jenatton, J. Mairal, and G. Obozinski, "Optimization with sparsity-inducing penalties," Found. Trends Mach. Learn., vol. 4, no. 1, pp. 1-106, Jan. 2012
34. I. Rish, and G. Grabarnik, Sparse Modeling: Theory, Algorithms, and Applications. Boca Raton, FL, USA: CRC Press, 2014
35. I. Rish, G. A. Cecchi, A. Lozano, and A. Niculescu-Mizil, Practical Applications of Sparse Modeling. Cambridge, MA, USA: MIT Press, 2014
36. Z. Zhang, Y. Xu, J. Yang, X. Li, and D. Zhang, "A survey of sparse representation: Algorithms, and applications," IEEE Access, vol. 3, pp. 490-530, May 2015
37. R. Tibshirani, "Regression shrinkage, and selection via the Lasso," J. Roy. Statist. Soc. B (Methodol.), vol. 58, no. 1, pp. 267-288, 1996
38. Y. Li, J. Si, G. Zhou, S. Huang, and S. Chen, "FREL: A stable feature selection algorithm," IEEE Trans. Neural Netw. Learn. Syst., vol. 26, no. 7, pp. 1388-1402, Jul. 2015
39. D. L. Donoho, "For most large underdetermined systems of linear equations the minimal 1-norm solution is also the sparsest solution," Commun. Pure Appl. Math., vol. 59, no. 6, pp. 797-829, 2006
40. A. Destrero, C. De Mol, F. Odone, and A. Verri, "A regularized approach to feature selection for face detection," in Proc. 8th Asian Conf. Comput. Vis., 2007, pp. 881-890
41. H. Zou, "The adaptive Lasso, and its oracle properties," J. Amer. Statist. Assoc., vol. 101, no. 476, pp. 1418-1429, Dec. 2006
42. R. Tibshirani, M. Saunders, S. Rosset, J. Zhu, and K. Knight, "Sparsity, and smoothness via the fused Lasso," J. Roy. Statist. Soc. B (Statist. Methodol.), vol. 67, no. 1, pp. 91-108, 2005
43. Z. Sun, F. Wang, and J. Hu, "LINKAGE: An approach for comprehensive risk prediction for care management," in Proc. 21st ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2015, pp. 1145-1154
44. S. Petry, C. Flexeder, and G. Tutz, "Pairwise fused Lasso," Dept. Statist., Ludwig Maximilian Univ. Munich, Munich, Germany, Tech. Rep. 102, 2011
45. J. Huang, J. L. Horowitz, and S. Ma, "Asymptotic properties of bridge estimators in sparse high-dimensional regression models," Ann. Statist., vol. 36, no. 2, pp. 587-613, 2008
46. H. Zou, and T. Hastie, "Regularization, and variable selection via the elastic net," J. Roy. Statist. Soc. B (Statist. Methodol.), vol. 67, no. 2, pp. 301-320, 2005
47. Z. Liu, et al., "Sparse logistic regression with Lp penalty for biomarker identification," Statist. Appl. Genet. Molecular Biol., vol. 6, no. 1, pp. 1-20, 2007
48. L. Wang, Z. Sun, and T. Tan, "Robust regularized feature selection for iris recognition via linear programming," in Proc. 21st Int. Conf. Pattern Recognit., Nov. 2012, pp. 3358-3361
49. Z. Sun, L. Wang, and T. Tan, "Ordinal feature selection for iris, and palmprint recognition," IEEE Trans. Image Process., vol. 23, no. 9, pp. 3922-3934, Sep. 2014
50. E. Grave, G. Obozinski, and F. Bach, "Trace Lasso: A trace norm regularization for correlated designs," in Proc. Adv. Neural Inf. Process. Syst., 2011, pp. 2187-2195
51. J. Ye, and J. Liu, "Sparse methods for biomedical data," ACM SIGKDD Explorations Newslett., vol. 14, no. 1, pp. 4-15, 2012
52. M. Yuan, and Y. Lin, "Model selection, and estimation in regression with grouped variables," J. Roy. Statist. Soc. B (Statist. Methodol.), vol. 68, no. 1, pp. 49-67, 2006
53. S. Zhang, J. Huang, H. Li, and D. N. Metaxas, "Automatic image annotation, and retrieval using group sparsity," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 42, no. 3, pp. 838-849, Jun. 2012
54. R. Jenatton, J.-Y. Audibert, and F. Bach, "Structured variable selection with sparsity-inducing norms," J. Mach. Learn. Res., vol. 12, pp. 2777-2824, Feb. 2011
55. J. Huang, T. Zhang, and D. Metaxas, "Learning with structured sparsity," J. Mach. Learn. Res., vol. 12, pp. 3371-3412, Jan. 2011
56. S. Kim, and E. P. Xing, "Tree-guided group Lasso for multi-task regression with structured sparsity," in Proc. 27th Int. Conf. Mach. Learn., 2010, pp. 543-550
57. J. Zhou, J. Liu, V. A. Narayan, and J. Ye, "Modeling disease progression via fused sparse group Lasso," in Proc. 18th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2012, pp. 1095-1103
58. D. Kong, R. Fujimaki, J. Liu, F. Nie, and C. Ding, "Exclusive feature learning on arbitrary structures via 1,2-norm," in Proc. Adv. Neural Inf. Process. Syst., 2014, pp. 1655-1663
59. J. Liu, and J. Ye, "Moreau-Yosida regularization for grouped tree structure learning," in Proc. Adv. Neural Inf. Process. Syst., 2010, pp. 1459-1467
60. M. Belkin, and P. Niyogi, "Laplacian eigenmaps for dimensionality reduction, and data representation," Neural Comput., vol. 15, no. 6, pp. 1373-1396, 2003
61. M. Slawski, W. Zu Castell, and G. Tutz, "Feature selection guided by structural information," Ann. Appl. Statist., vol. 4, no. 2, pp. 1056-1080, 2010
62. S. Kim, and E. P. Xing, "Statistical estimation of correlated genome associations to a quantitative trait network," PLoS Genet., vol. 5, no. 8, p. e1000587, 2009
63. Y. Zhu, X. Shen, and W. Pan, "Simultaneous grouping pursuit, and feature selection over an undirected graph," J. Amer. Statist. Assoc., vol. 108, no. 502, pp. 713-725, 2013
64. S. Yang, L. Yuan, Y.-C. Lai, X. Shen, P. Wonka, and J. Ye, "Feature grouping, and selection over an undirected graph," in Proc. 18th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2012, pp. 922-930
65. X. Wu, K. Yu, W. Ding, H. Wang, and X. Zhu, "Online feature selection with streaming features," IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 5, pp. 1178-1192, May 2013
66. S. Perkins, and J. Theiler, "Online feature selection using grafting," in Proc. 20th Int. Conf. Mach. Learn., 2003, pp. 592-599
67. C. Ding, D. Zhou, X. He, and H. Zha, "R1-PCA: Rotational invariant L1-norm principal component analysis for robust subspace factorization," in Proc. 23rd Int. Conf. Mach. Learn., 2006, pp. 281-288
68. A. Argyriou, A. Evgeniou, and M. Pontil, "Multi-task feature learning," in Proc. Adv. Neural Inf. Process. Syst., 2007, pp. 41-48
69. G. Obozinski, B. Taskar, and M. Jordan, "Multi-task feature selection," Dept. Statist, Univ. California, Berkeley, Berkeley, CA, USA, Tech. Rep. 743, 2006
70. Y. Han, Y. Yang, Y. Yan, Z. Ma, N. Sebe, and X. Zhou, "Semisupervised feature selection via spline regression for video semantic recognition," IEEE Trans. Neural Netw. Learn. Syst., vol. 26, no. 2, pp. 252-264, Feb. 2015
71. D. Luo, C. Ding, and H. Huang, "Towards structural sparsity: An explicit 2/0 approach," in Proc. IEEE 10th Int. Conf. Data Mining, Dec. 2010, pp. 344-353
72. L. Wang, S. Chen, and Y. Wang, "A unified algorithm for mixed l2, p -minimizations, and its application in feature selection," Comput. Optim. Appl., vol. 58, no. 2, pp. 409-421, 2014
73. M. Zhang, C. Ding, Y. Zhang, and F. Nie, "Feature selection at the discrete limit," in Proc. 38th AAAI Conf. Artif. Intell., 2014, pp. 1355-1361
74. B. A. Turlach, W. N. Venables, and S. J. Wright, "Simultaneous variable selection," Technometrics, vol. 47, no. 3, pp. 349-363, 2005
75. J. A. Tropp, "Algorithms for simultaneous sparse approximation. Part II: Convex relaxation," Signal Process., vol. 86, no. 3, pp. 589-602, Mar. 2006
76. A. Quattoni, M. Collins, and T. Darrell, "Transfer learning for image classification with sparse prototype representations," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2008, pp. 1-8
77. M. Schmidt, K. Murphy, G. Fung, and R. Rosales, "Structure learning in random fields for heart motion abnormality detection," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2008, pp. 1-8
78. Y. Jia, M. Salzmann, and T. Darrell, "Factorized latent spaces with structured sparsity," in Proc. Adv. Neural Inf. Process. Syst., 2010, pp. 982-990
79. A. Quattoni, X. Carreras, M. Collins, and T. Darrell, "An efficient projection for l1,8 regularization," in Proc. 26th Annu. Int. Conf. Mach. Learn., 2009, pp. 857-864
80. Y. Zhang, and D.-Y. Yeung, "A convex formulation for learning task relationships in multi-task learning," in Proc. Conf. Uncertainty Artif. Intell., 2010, pp. 733-742
81. Y. Zhou, R. Jin, and S. C. H. Hoi, "Exclusive Lasso for multi-task feature selection," in Proc. 13th Int. Conf. Artif. Intell. Statist., 2010, pp. 988-995
82. Z. Zhao, L. Wang, H. Liu, and J. Ye, "On similarity preserving feature selection," IEEE Trans. Knowl. Data Eng., vol. 25, no. 3, pp. 619-632, Mar. 2013
83. X. Liu, L. Wang, J. Zhang, J. Yin, and H. Liu, "Global, and local structure preservation for feature selection," IEEE Trans. Neural Netw. Learn. Syst., vol. 25, no. 6, pp. 1083-1095, Jun. 2013
84. P. Zhu, W. Zuo, L. Zhang, Q. Hu, and S. C. K. Shiu, "Unsupervised feature selection by regularized self-representation," Pattern Recognit., vol. 48, no. 2, pp. 438-446, 2015
85. W. Liu, P. P. Pokharel, and J. C. Príncipe, "Correntropy: Properties, and applications in non-Gaussian signal processing," IEEE Trans. Signal Process., vol. 55, no. 11, pp. 5286-5298, Nov. 2007
86. R. He, T. Tan, L. Wang, and W.-S. Zheng, "l2,1 regularized correntropy for robust feature selection," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2012, pp. 2504-2511
87. M. Nikolova, and M. K. Ng, "Analysis of half-quadratic minimization methods for signal, and image recovery," SIAM J. Sci. Comput., vol. 27, no. 3, pp. 937-966, 2005
88. S. Xiang, F. Nie, G. Meng, C. Pan, and C. Zhang, "Discriminative least squares regression for multiclass classification, and feature selection," IEEE Trans. Neural Netw. Learn. Syst., vol. 23, no. 11, pp. 1738-1754, Nov. 2012
89. C. Hou, F. Nie, D. Yi, and Y. Wu, "Feature selection via joint embedding learning, and sparse regression," in Proc. 22nd Int. Joint Conf. Artif. Intell., 2011, pp. 1324-1329
90. C. Hou, F. Nie, X. Li, D. Yi, and Y. Wu, "Joint embedding learning, and sparse regression: A framework for unsupervised feature selection," IEEE Trans. Cybern., vol. 44, no. 6, pp. 793-804, Jun. 2014
91. S. T. Roweis, and L. K. Saul, "Nonlinear dimensionality reduction by locally linear embedding," Science, vol. 290, no. 5500, pp. 2323-2326, Dec. 2000
92. D. Cai, C. Zhang, and X. He, "Unsupervised feature selection for multicluster data," in Proc. ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2010, pp. 333-342
93. Z. Zhao, L. Wang, and H. Liu, "Efficient spectral feature selection with minimum redundancy," in Proc. 24th AAAI Conf. Artif. Intell., 2010, pp. 673-678
94. X. Zhu, X. Wu, W. Ding, and S. Zhang, "Feature selection by joint graph sparse coding," in Proc. SIAM Int. Conf. Data Mining, 2013, pp. 803-811
95. H. Li, T. Jiang, and K. Zhang, "Efficient, and robust feature extraction by maximum margin criterion," IEEE Trans. Neural Netw., vol. 17, no. 1, pp. 157-165, Feb. 2006
96. J. Liu, S. Chen, X. Tan, and D. Zhang, "Comments on efficient, and robust feature extraction by maximum margin criterion," IEEE Trans. Neural Netw., vol. 18, no. 6, pp. 1862-1864, Nov. 2007
97. S. Yang, C. Hou, F. Nie, and Y. Wu, "Unsupervised maximum margin feature selection via L2,1-norm minimization," Neural Comput. Appl., vol. 21, no. 7, pp. 1791-1799, 2012
98. Y. Yang, H. T. Shen, Z. Ma, Z. Huang, and X. Zhou, "l2,1-norm regularized discriminative feature selection for unsupervised learning," in Proc. 22nd Int. Joint Conf. Artif. Intell., 2011, pp. 1589-1594
99. Q. Gu, Z. Li, and J. Han, "Joint feature selection, and subspace learning," in Proc. Int. Joint Conf. Artif. Intell., 2011, pp. 1294-1299
100. S. Yan, D. Xu, B. Zhang, H.-J. Zhang, Q. Yang, and S. Lin, "Graph embedding, and extensions: A general framework for dimensionality reduction," IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 1, pp. 40-51, Jan. 2007
101. Q. Gu, and J. Han, "Towards feature selection in network," in Proc. 20th ACM Int. Conf. Inf. Knowl. Manage., 2011, pp. 1175-1184
102. F. Chung, Spectral Graph Theory, vol. 92. Providence, RI, USA: AMS, 1997
103. M. Masaeli, G. Fung, and J. G. Dy, "From transformation-based dimensionality reduction to feature selection," in Proc. 27th Int. Conf. Mach. Learn., 2010, pp. 751-758
104. X. Cai, F. Nie, and H. Huang, "Exact top-k feature selection via 2,0-norm constraint," in Proc. Int. Joint Conf. Artif. Intell., 2013, pp. 1240-1246
105. D. Hernández-Lobato, J. M. Hernández-Lobato, and Z. Ghahramani, "A probabilistic model for dirty multi-task feature selection," in Proc. 32nd Int. Conf. Mach. Learn., 2015, pp. 1073-1082
106. D. Han, and J. Kim, "Unsupervised simultaneous orthogonal basis clustering feature selection," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2015, pp. 5016-5023
107. J. Liu, S. Ji, and J. Ye, "Multi-task feature learning via efficient l2,1-norm minimization," in Proc. 25th Conf. Uncertainty Artif. Intell., 2009, pp. 339-348
108. H. Wang, et al., "Sparse multi-task regression, and feature selection to identify brain imaging predictors for memory performance," in Proc. IEEE Int. Conf. Comput. Vis., Nov. 2011, pp. 557-562
109. S. Lee, J. Zhu, and E. P. Xing, "Adaptive multi-task Lasso: With application to eQTL detection," in Proc. Adv. Neural Inf. Process. Syst., 2010, pp. 1306-1314
110. J. Tang, and H. Liu, "Feature selection with linked data in social media," in Proc. SIAM Int. Conf. Data Mining, 2012, pp. 118-128
111. Q. Gu, Z. Li, and J. Han, "Linear discriminant dimensionality reduction," in Proc. Eur. Conf. Mach. Learn., 2011, pp. 549-564
112. G. Obozinski, B. Taskar, and M. I. Jordan, "Joint covariate selection, and joint subspace selection for multiple classification problems," Statist. Comput., vol. 20, no. 2, pp. 231-252, Apr. 2010
113. Y. Liang, S. Liao, L. Wang, and B. Zou, "Exploring regularized feature selection for person specific face verification," in Proc. IEEE Int. Conf. Comput. Vis., Nov. 2011, pp. 1676-1683
114. T. Jebara, "Multitask sparsity via maximum entropy discrimination," J. Mach. Learn. Res., vol. 12, no. 1, pp. 75-110, 2011
115. X. Chang, F. Nie, Y. Yang, and H. Huang, "A convex formulation for semi-supervised multi-label feature selection," in Proc. 28th AAAI Conf. Artif. Intell., 2014, pp. 1171-1177
116. Y. Guo, and W. Xue, "Probabilistic multi-label classification with sparse feature learning," in Proc. Int. Joint Conf. Artif. Intell., 2013, pp. 1373-1379
117. L.-L. Huang, J. Tang, S.-B. Chen, C. Ding, and B. Luo, "An efficient algorithm for feature selection with feature correlation," in Proc. Intell. Sci. Intell. Data Eng., 2013, pp. 639-646
118. S. Ji, L. Tang, S. Yu, and J. Ye, "A shared-subspace learning framework for multi-label classification," ACM Trans. Knowl. Discovery Data, vol. 4, no. 2, pp. 1-29, 2010
119. Z. Ma, F. Nie, Y. Yang, J. R. R. Uijlings, and N. Sebe, "Web image annotation via subspace-sparsity collaborated feature selection," IEEE Trans. Multimedia, vol. 14, no. 4, pp. 1021-1030, Aug. 2012
120. C. Xu, D. Tao, and C. Xu, "Multi-view intact space learning," IEEE Trans. Pattern Anal. Mach. Intell., vol. 37, no. 12, pp. 2531-2544, Dec. 2015
121. L. Xiao, Z. Sun, R. He, and T. Tan, "Coupled feature selection for cross-sensor iris recognition," in Proc. IEEE 6th Int. Conf. Biometrics, Theory, Appl. Syst., Sep./Oct. 2013, pp. 1-6
122. L. Xiao, Z. Sun, R. He, and T. Tan, "Margin based feature selection for cross-sensor iris recognition via linear programming," in Proc. 2nd IAPR Asia Conf. Pattern Recognit., 2013, pp. 246-250
123. H. Wang, F. Nie, H. Huang, and C. Ding, "Heterogeneous visual features fusion via sparse multimodal machine," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2013, pp. 3097-3102
124. H. Wang, F. Nie, and H. Huang, "Multi-view clustering, and feature learning via structured sparsity," in Proc. 30th Int. Conf. Mach. Learn., 2013, pp. 352-360
125. H. Wang, et al., "Identifying quantitative trait loci via group-sparse multitask regression, and feature selection: An imaging genetics study of the ADNI cohort," Bioinformatics, vol. 28, no. 2, pp. 229-237, 2012
126. H. Wang, et al., "Identifying disease sensitive, and quantitative traitrelevant biomarkers from multidimensional heterogeneous imaging genetics data via sparse multimodal multitask learning," Bioinformatics, vol. 28, no. 12, pp. i127-i136, 2012
127. J. Gui, D. Tao, Z. Sun, Y. Luo, X. You, and Y. Y. Tang, "Group sparse multiview patch alignment framework with view consistency for image classification," IEEE Trans. Image Process., vol. 23, no. 7, pp. 3126-3137, Jul. 2014
128. T. Liu, and D. Tao, "Classification with noisy labels by importance reweighting," IEEE Trans. Pattern Anal. Mach. Intell., vol. 38, no. 3, pp. 447-461, Mar. 2016
129. P. S. Bradley, and O. L. Mangasarian, "Feature selection via concave minimization, and support vector machines," in Proc. 15th Int. Conf. Mach. Learn., 1998, pp. 82-90
130. L. Laporte, R. Flamary, S. Canu, S. Déjean, and J. Mothe, "Nonconvex regularizations for feature selection in ranking with sparse SVM," IEEE Trans. Neural Netw. Learn. Syst., vol. 25, no. 6, pp. 1118-1130, Jun. 2014
131. L. Wang, J. Zhu, and H. Zou, "Hybrid huberized support vector machines for microarray classification, and gene selection," Bioinformatics, vol. 24, no. 3, pp. 412-419, 2008
132. M. Tan, L. Wang, and I. W. Tsang, "Learning sparse SVM for feature selection on very high dimensional datasets," in Proc. 27th Int. Conf. Mach. Learn., 2010, pp. 1047-1054
133. G. Fung, and O. L. Mangasarian, "Data selection for support vector machine classifiers," in Proc. ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2000, pp. 64-70
134. B.-Y. Sun, Z.-H. Zhu, J. Li, and B. Linghu, "Combined feature selection, and cancer prognosis using support vector machine regression," IEEE-ACM Trans. Comput. Biol. Bioinformatics, vol. 8, no. 6, pp. 1671-1677, Nov./Dec. 2011
135. Z. Liu, S. Lin, and M. T. Tan, "Sparse support vector machines with L p penalty for biomarker identification," IEEE-ACM Trans. Comput. Biol. Bioinformatics, vol. 7, no. 1, pp. 100-107, Jan./Mar. 2010
136. G. C. Cawley, N. L. C. Talbot, and M. Girolami, "Sparse multinomial logistic regression via Bayesian L1 regularisation," in Proc. Adv. Neural Inf. Process. Syst., 2007, pp. 209-216
137. S. K. Shevade, and S. S. Keerthi, "A simple, and efficient algorithm for gene selection using sparse logistic regression," Bioinformatics, vol. 19, no. 17, pp. 2246-2253, 2003
138. G. C. Cawley, and N. L. C. Talbot, "Gene selection in cancer classification using sparse logistic regression with Bayesian regularization," Bioinformatics, vol. 22, no. 19, pp. 2348-2355, 2006
139. Y. Qian, J. Zhou, M. Ye, and Q. Wang, "Structured sparse model based feature selection, and classification for hyperspectral imagery," in Proc. IEEE Int. Geosci. Remote Sens. Symp., Jul. 2011, pp. 1771-1774
140. X. Cai, F. Nie, H. Huang, and C. Ding, "Multi-class 2,1-norm support vector machine," in Proc. IEEE 11th Int. Conf. Data Mining, Dec. 2011, pp. 91-100
141. Z. Li, Y. Yang, J. Liu, X. Zhou, and H. Lu, "Unsupervised feature selection using nonnegative spectral analysis," in Proc. AAAI Conf. Artif. Intell., 2012, pp. 1026-1032
142. Y. Yang, H. T. Shen, F. Nie, R. Ji, and X. Zhou, "Nonnegative spectral clustering with discriminative regularization," in Proc. AAAI Conf. Artif. Intell., 2011, pp. 555-560
143. J. Shi, and J. Malik, "Normalized cuts, and image segmentation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 8, pp. 888-905, Aug. 2000
144. S. X. Yu, and J. Shi, "Multiclass spectral clustering," in Proc. 9th IEEE Int. Conf. Comput. Vis., Oct. 2003, pp. 313-319
145. D. M. Witten, and R. Tibshirani, "A framework for feature selection in clustering," J. Amer. Statistical Assoc., vol. 105, no. 490, pp. 713-726, 2010
146. H. Zeng, and Y.-M. Cheung, "Feature selection, and kernel learning for local learning-based clustering," IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 8, pp. 1532-1547, Aug. 2011
147. X. He, D. Cai, and P. Niyogi, "Laplacian score for feature selection," in Proc. Adv. Neural Inf. Process. Syst., 2006, pp. 507-514
148. J. Xu, G. Yang, H. Man, and H. He, "L1 graph based on sparse coding for feature selection," in Proc. 10th Int. Symp. Neural Netw., 2013, pp. 594-601
149. A. Y. Ng, "Feature selection, L1 vs. L2 regularization, and rotational invariance," in Proc. 21st Int. Conf. Mach. Learn., 2004, pp. 78-85
150. Y. Sun, S. Todorovic, and S. Goodison, "Local-learning-based feature selection for high-dimensional data analysis," IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 9, pp. 1610-1626, Sep. 2010
151. L. Wolf, and A. Shashua, "Feature selection for unsupervised, and supervised inference: The emergence of sparsity in a weight-based approach," J. Mach. Learn. Res., vol. 6, pp. 1855-1887, Jan. 2005
152. Q. Mao, and I. W.-H. Tsang, "A feature selection method for multivariate performance measures," IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 9, pp. 2051-2063, Sep. 2013
153. S. A. Nene, S. K. Nayar, and H. Murase, "Columbia object image library (COIL-20)," Dept. Comput. Sci., Columbia Univ., New York, NY, USA, Tech. Rep. CUCS-005-96, 1996
154. C. L. Nutt, et al., "Gene expression-based classification of malignant gliomas correlates better with survival than histological classification," Cancer Res., vol. 63, no. 7, pp. 1602-1607, 2003
155. A. Bhattacharjee, et al., "Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses," Proc. Nat. Acad. Sci. USA, vol. 98, no. 24, pp. 13790-13795, 2001
156. S. A. Armstrong, et al., "MLL translocations specify a distinct gene expression profile that distinguishes a unique leukemia," Nature Genet., vol. 30, no. 1, pp. 41-47, 2002
157. L. Sun, et al., "Neuronal, and glioma-derived stem cell factor induces angiogenesis within the brain," Cancer Cell, vol. 9, no. 4, pp. 287-300, 2006
158. A. Martínez, and R. Benavente, "The AR face database," Dept. Elect. Comput. Eng., Ohio State Univ., Columbus, OH USA, Tech. Rep. 24, Jun. 1998
159. L. Čehovin, and Z. Bosnić, "Empirical evaluation of feature selection methods in classification," Intell. Data Anal., vol. 14, no. 3, pp. 265-281, 2010
160. T. M. Cover, and J. A. Thomas, Elements of Information Theory, 2nd ed. New York, NY, USA: Wiley, 2006
161. H. Peng, F. Long, and C. Ding, "Feature selection based on mutual information criteria of max-dependency, max-relevance, and minredundancy," IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 8, pp. 1226-1238, Aug. 2005
162. I. Kononenko, "Estimating attributes: Analysis, and extensions of RELIEF," in Proc. Eur. Conf. Mach. Learn., 1994, pp. 171-182
163. D. C. Montgomery, G. C. Runger, and N. F. Hubele, Engineering Statistics. New York, NY, USA: Wiley, 2009
164. L. Deng, J. Pei, J. Ma, and D. L. Lee, "A rank sum test method for informative gene discovery," in Proc. 10th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2004, pp. 410-419
165. D. Tao, X. Lin, L. Jin, and X. Li, "Principal component 2-D long shortterm memory for font recognition on single Chinese characters," IEEE Trans. Cybern., vol. 46, no. 3, pp. 756-765, Mar. 2016
166. E. P. Xing, M. I. Jordan, and R. M. Karp, "Feature selection for highdimensional genomic microarray data," in Proc. 18th Int. Conf. Mach. Learn., 2001, pp. 601-608
167. W. Cochran, Sampling Techniques. Hoboken, NJ, USA: Wiley, 2007
168. B. Gu, F. Hu, and H. Liu, "Sampling: Knowing whole from its part," in Instance Selection, and Construction for Data Mining. Boston, MA, USA: Kluwer Academic Publishers, 2001, pp. 21-38
169. H. Liu, H. Motoda, and L. Yu, "Feature selection with selective sampling," in Proc. Int. Conf. Mach. Learn., 2002, pp. 395-402