Sparse discriminative feature selection

Pattern Recognition

Volumn 48, Issue 5, 2015, Pages 1827-1835

  Yan, Hui ^a   Yang, Jian ^a  

^a NANJING UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

Discriminative learning; Joint feature selection; Sparse representation based classification

Indexed keywords

BENCHMARKING; COMBINATORIAL OPTIMIZATION; ITERATIVE METHODS;

1-NORM MINIMIZATIONS; DIMENSION REDUCTION; DISCRIMINATIVE FEATURES; DISCRIMINATIVE LEARNING; FEATURE SELECTION METHODS; ITERATIVE ALGORITHM; SPARSE REPRESENTATION; SPARSE REPRESENTATION BASED CLASSIFICATIONS;

FEATURE EXTRACTION;

EID: 84921804171     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2014.10.021     Document Type: Article

References (38)

1. R. Duda, P. Hart, and D. Stork Pattern Recognit. 2nd edition 2001 John Wiley & Sons New York, USA
2. X. He, D. Cai, P. Niyogi, Laplacian score for feature selection, in: Neural Information Processing Systems, Vancouver, British Columbia, Canada, 2005.
3. Z. Zhao, H. Liu, Sprectral feature selection for supervised and unsupervised learning, in: International Conference on Machine Learning, Corvallis, Oregon, USA, 2007.
4. M. Masaeli, G. Fung, J.G. Dy, From transformation-based dimensionality reduction to feature selection, in: International Conference on Machine Learning, Haifa, Israel, 2010.
5. H. Liu, X. Wu, S. Zhang, Feature selection using hierarchical feature clustering, in: Conference on Information and Knowledge Management, Glasgow, Scotland, UK, 2011.
6. I. Guyon, and A. Elisseeffi An introduction to variable and feature selection J. Mach. Learn. Res. 3 2003 1157 1182
7. A. Rakotomamonjy Variable selection using svm based criteria J. Mach. Learn. Res. 3 2003 1357 1370
8. V. Vapnik Statistical Learning Theory 1998 Wiley New York
9. J. Zhu, S. Rosset, T. Hastie, R. Tibshirani, 1-norm support vector machines, in: Advances in Neural Information Processing Systems, Vancouver, British Columbia, Canada, 2003.
10. , 1-norms minimization, in: Advances in Neural Information Processing Systems, Vancouver, BC, Canada, 2010.
11. Z.G. Ma, F.P. Nie, Y. Yang, J.R.R. Uijlings, and N. Sebe Web image annotation via subspace-sparsity collaborated feature selection IEEE Trans. Multimed. 14 4 2012 1021 1030
12. Q.Q. Gu, Z.H. Li, J.W. Han, Joint feature selection and subspace learning, in: International Joint Conference on Artificial Intelligence, Barcelona, Spain, 2011.
13. J. Cai, E.J. Candès, and Z. Shen A singular value thresholding algorithm for matrix completion SIAM J. Optim. 20 4 2010 1956 1982
14. , 1-norm regularized discriminative feature selection for unsupervised learning, in: AAAI Conference on Artificial Intelligence, San Francisco, California, USA, 2011.
15. Y. Cong, J.S. Yuan, and J.B. Luo Towards scalable summarization of consumer videos via sparse dictionary selection IEEE Trans. Multimed. 14 1 2012 66 75
16. A. Krause, V. Cevher, Submodular dictionary selection for sparse representation, in: International Conference on Machine Learning, Haifa, Israel, 2010.
17. C.L. Yang, J.L. Shen, J.Y. Peng, and J.P. Fan Image collection summarization via dictionary learning for sparse representation Pattern Recognit. 46 3 2013 948 961
18. B.K. Bao, G.Y. Zhu, J.L. Shen, and S.C. Yan Robust image analysis with sparse representation on quantized visual features IEEE Trans. Image Process. 22 3 2013 860 871
19. J.S. Huang, H.R. Liu, J.L. Shen, S.C. Yan, Towards efficient sparse coding for scalable image annotation, in: ACM Multimedia, Barcelona, Catalunya, Spain, 2013, pp. 947-956.
20. 1-optimizer in pattern classification Pattern Recognit. 45 3 2012 1104 1118
21. L.S. Qiao, S.C. Chen, and X.Y. Tan Sparsity preserving projections with applications to face recognition Pattern Recognit. 43 1 2010 331 341
22. J. Yang, D. Chu, L. Zhang, Y. Xu, and J.Y. Yang Representation classifier steered discriminative projection with applications to face recognition IEEE Trans. Neural Netw. Learn. Syst. 24 7 2013 1023 1035
23. C.Y. Lu, and D.S. Huang Optimized projections for sparse representation based classification Neurocomputing 113 2013 213 219
24. J. Gui, Z.N. Sun, W. Jia, R.X. Hu, Y.K. Lei, and S.W. Ji Discriminant sparse neighborhood preserving embedding for face recognition Pattern Recognit. 45 8 2012 2884 2893
25. J. Wright, A.Y. Yang, A. Ganesh, S.S. Sastry, and Y. Ma Robust face recognition via sparse representation IEEE Trans. Pattern Anal. Mach. Intell. 31 2 2009 210 227
26. Z. Lin, M. Chen, L. Wu, Y. Ma, The Augmented Lagrange Multiplier Method for Exact Recovery of Corrupted Low-rank Matrices, Technical Report UILU-ENG-09-2215, University of Illinois at Urbana-Champaign.
27. L.M. Zhang, S. Chen, and L. Qiao Graph optimization for dimensionality reduction with sparsity constraints Pattern Recognit. 45 3 2012 1205 1210
28. 1 norm minimization problems when the solution may be sparse IEEE Trans. Inf. Theory 54 11 2008 4789 4812
29. K. Kira, and L.A. Rendell A practical approach to feature selection Mach. Learn. 1992 249 256
30. K. Fukunaga Introduction to Statistical Pattern Recognition 2nd edition 1990 Academic Press Professional, Inc. San Diego, CA, USA
31. D. Cai, X.F. He, J.W. Han, Spectral regression: a unified approach for sparse subspace learning, in: International Conference on Machine Learning, Corvallis, OR, USA, 2007.
32. Y. Zhang Recent advances in alternating direction methods practice and theory Tutorial 3 2010 1 3
33. D. Bertsekas Constrained Optimization and Lagrange Multiplier Methods 1982 Academic Press San Diego, CA, USA
34. G.W. Stewart Matrix Algorithms, Volume II: Eigensystems 2001 SIAM Philadelphia
35. T. Sim, S. Baker, and M. Bsat The cmu pose, illumination, and expression database IEEE Trans. Pattern Anal. Mach. Intell. 25 12 2003 1615 1618
36. H. Peng, F. Long, and C. Ding Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy IEEE Trans. Pattern Anal. Mach. Intell. 27 8 2005 1226 1238
37. G. Brown, A new perspective for information theoretic feature selection, in: International Conference on Artificial Intelligence and Statistics, Clearwater Beach, Florida USA, 2009.
38. X.F. He, P. Niyogi, Locality preserving projections, in: Advances in Neural Information Processing Systems, Vancouver, British Columbia, Canada, 2003.