Robust Joint Graph Sparse Coding for Unsupervised Spectral Feature Selection

IEEE Transactions on Neural Networks and Learning Systems

Volumn 28, Issue 6, 2017, Pages 1263-1275

  Zhu, Xiaofeng ^a   Li, Xuelong ^b   Zhang, Shichao ^c   Ju, Chunhua ^c   Wu, Xindong ^d  

^a GUANGXI NORMAL UNIVERSITY   (China)

^b XI AN INSTITUTE OF OPTICS AND PRECISION MECHANICS   (China)

^c ZHEJIANG GONGSHANG UNIVERSITY   (China)

^d University of Vermont ^*  (United States)

Author keywords

Dimensionality reduction; manifold learning; regression; sparse coding

Indexed keywords

CLASSIFICATION (OF INFORMATION); CODES (SYMBOLS); DATA MINING; FEATURE EXTRACTION; NEAREST NEIGHBOR SEARCH;

DICTIONARY LEARNING; K-NEAREST NEIGHBOR CLASSIFICATION; LOCAL STRUCTURE; OBJECTIVE FUNCTIONS; OPTIMIZATION SOLUTION; SPARSE REGRESSION; STATE-OF-THE-ART ALGORITHMS; SUBSPACE LEARNING;

GRAPH THEORY;

EID: 84960157230     PISSN: 2162237X     EISSN: 21622388     Source Type: Journal    
DOI: 10.1109/TNNLS.2016.2521602     Document Type: Article

References (62)

1. G. Yu, G. Zhang, Z. Zhang, Z. Yu, and L. Deng, "Semi-supervised classification based on subspace sparse representation," Knowl. Inf. Syst., vol. 43, no. 1, pp. 81-101, 2015.
2. J. Yu, X. Gao, D. Tao, X. Li, and K. Zhang, "A unified learning framework for single image super-resolution," IEEE Trans. Neural Netw. Learn. Syst., vol. 25, no. 4, pp. 780-792, Apr. 2014.
3. Q. Zhu, L. Shao, X. Li, and L. Wang, "Targeting accurate object extraction from an image: A comprehensive study of natural image matting," IEEE Trans. Neural Netw. Learn. Syst., vol. 26, no. 2, pp. 185-207, Feb. 2015.
4. p-norm robust matrix completion for missing value recovery," Knowl. Inf. Syst., vol. 42, no. 3, pp. 525-544, 2013.
5. X. Lu and X. Li, "Multiresolution imaging," IEEE Trans. Cybern., vol. 44, no. 1, pp. 149-160, Jan. 2014.
6. X. Lu, Y. Wang, and Y. Yuan, "Sparse coding from a Bayesian perspective," IEEE Trans. Neural Netw. Learn. Syst., vol. 24, no. 6, pp. 929-939, Jun. 2013.
7. L. Shao, L. Liu, and X. Li, "Feature learning for image classification via multiobjective genetic programming," IEEE Trans. Neural Netw. Learn. Syst., vol. 25, no. 7, pp. 1359-1371, Jul. 2014.
8. X. Zhu, S. Zhang, Z. Jin, Z. Zhang, and Z. Xu, "Missing value estimation for mixed-attribute data sets," IEEE Trans. Knowl. Data Eng., vol. 23, no. 1, pp. 110-121, Jan. 2011.
9. Y. Yang, Z. Ma, F. Nie, X. Chang, and A. G. Hauptmann, "Multi-class active learning by uncertainty sampling with diversity maximization," Int. J. Comput. Vis., vol. 113, no. 2, pp. 113-127, 2015.
10. R. Hong, M. Wang, Y. Gao, D. Tao, X. Li, and X. Wu, "Image annotation by multiple-instance learning with discriminative feature mapping and selection," IEEE Trans. Cybern., vol. 44, no. 5, pp. 669-680, May 2014.
11. A. K. Farahat, A. Elgohary, A. Ghodsi, and M. S. Kamel, "Greedy column subset selection for large-scale data sets," Knowl. Inf. Syst., vol. 45, no. 1, pp. 1-34, 2015.
12. Y. Yang, F. Nie, D. Xu, J. Luo, Y. Zhuang, and Y. Pan, "A multimedia retrieval framework based on semi-supervised ranking and relevance feedback," IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 4, pp. 723-742, Apr. 2012.
13. P. Nirmala, L. R. Sulochana, and N. Rethnasamy, "Centrality measuresbased algorithm to visualize a maximal common induced subgraph in large communication networks," Knowl. Inf. Syst., vol. 46, no. 1, pp. 213-239, 2015.
14. M. Zheng et al., "Graph regularized sparse coding for image representation," IEEE Trans. Image Process., vol. 20, no. 5, pp. 1327-1336, May 2011.
15. Z. Ma, Y. Yang, N. Sebe, and A. G. Hauptmann, "Knowledge adaptation with partially shared features for event detection using few exemplars," IEEE Trans. Pattern Anal. Mach. Intell., vol. 36, no. 9, pp. 1789-1802, Sep. 2014.
16. X. Zhu, Z. Huang, H. T. Shen, J. Cheng, and C. Xu, "Dimensionality reduction by mixed kernel canonical correlation analysis," Pattern Recognit., vol. 45, no. 8, pp. 3003-3016, 2012.
17. X. Zhu, H.-I. Suk, S.-W. Lee, and D. Shen, "Canonical feature selection for joint regression and multi-class identification in Alzheimer's disease diagnosis," Brain Imag. Behavior, pp. 1-11, Aug. 2015.
18. D. Cai, C. Zhang, and X. He, "Unsupervised feature selection for multi-cluster data," in Proc. 16th ACM SIGKDD Int. Conf. KDD, 2010, pp. 333-342.
19. S. Gao, I. W.-H. Tsang, L.-T. Chia, and P. Zhao, "Local features are not lonely-Laplacian sparse coding for image classification," in Proc. IEEE Conf. CVPR, Jun. 2010, pp. 3555-3561.
20. Y. Yang, Y.-T. Zhuang, F. Wu, and Y.-H. Pan, "Harmonizing hierarchical manifolds for multimedia document semantics understanding and crossmedia retrieval," IEEE Trans. Multimedia, vol. 10, no. 3, pp. 437-446, Apr. 2008.
21. Z. Zhao, L. Wang, and H. Liu, "Efficient spectral feature selection with minimum redundancy," in Proc. AAAI, 2010, pp. 55-60.
22. X. Zhu, H.-I. Suk, S.-W. Lee, and D. Shen, "Subspace regularized sparse multi-task learning for multi-class neurodegenerative disease identification," IEEE Trans. Biomed. Eng., to be published.
23. X. Zhu, X. Wu, W. Ding, and S. Zhang, "Feature selection by joint graph sparse coding," in Proc. SDM, 2013, pp. 803-811.
24. L. Shao, R. Yan, X. Li, and Y. Liu, "From heuristic optimization to dictionary learning: A review and comprehensive comparison of image denoising algorithms," IEEE Trans. Cybern., vol. 44, no. 7, pp. 1001-1013, Jul. 2014.
25. J. Mairal, F. Bach, J. Ponce, and G. Sapiro, "Online learning for matrix factorization and sparse coding," J. Mach. Learn. Res., vol. 11, pp. 19-60, Mar. 2010.
26. 2,1-norm regularized discriminative feature selection for unsupervised learning," in Proc. 22nd IJCAI, 2011, pp. 1589-1594.
27. X. Zhu, L. Zhang, and Z. Huang, "A sparse embedding and least variance encoding approach to hashing," IEEE Trans. Image Process., vol. 23, no. 9, pp. 3737-3750, Sep. 2014.
28. S. B. Kotsiantis, "Feature selection for machine learning classification problems: A recent overview," Artif. Intell. Rev., vol. 42, no. 1, pp. 1-20, 2011.
29. S. Zhang, H. Zhou, F. Jiang, and X. Li, "Robust visual tracking using structurally random projection and weighted least squares," IEEE Trans. Circuits Syst. Video Technol., vol. 25, no. 11, pp. 1749-1760, Nov. 2015.
30. W. Wang, D. Cai, L. Wang, Q. Huang, X. Xu, and X. Li, "Synthesized computational aesthetic evaluation of photos," Neurocomputing, vol. 172, pp. 244-252, Jan. 2016.
31. S. Foitong, P. Rojanavasu, B. Attachoo, and O. Pinngern, "Estimating optimal feature subsets using mutual information feature selector and rough sets," in Proc. 13th PAKDD, 2009, pp. 973-980.
32. R. Caruana and V. R. de Sa, "Benefitting from the variables that variable selection discards," J. Mach. Learn. Res., vol. 3, pp. 1245-1264, Mar. 2003.
33. X. He and P. Niyogi, "Locality preserving projections," in Proc. NIPS, 2003, pp. 197-204.
34. I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, "Gene selection for cancer classification using support vector machines," Mach. Learn., no. 46, no. 1, pp. 389-422, 2002.
35. S. Maldonado and R. Weber, "A wrapper method for feature selection using support vector machines," Inf. Sci., vol. 179, no. 13, pp. 2208-2217, 2009.
36. X. Li, L. Mou, and X. Lu, "Scene parsing from an MAP perspective," IEEE Trans. Cybern., vol. 45, no. 9, pp. 1876-1886, Sep. 2015.
37. Z. Tang, X. Zhang, X. Li, and S. Zhang, "Robust image hashing with ring partition and invariant vector distance," IEEE Trans. Inf. Forensics Security, vol. 11, no. 1, pp. 200-214, Jan. 2016.
38. J. Weston, A. Elisseeff, B. Schölkopf, and M. Tipping, "Use of the zero norm with linear models and kernel methods," J. Mach. Learn. Res., vol. 3, pp. 1439-1461, Jan. 2003.
39. 2,1-norm minimization," in Proc. 25th Conf. UAI, 2009, pp. 339-348.
40. 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.
41. 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.
42. X. Zhu, H.-I. Suk, and D. Shen, "A novel matrix-similarity based loss function for joint regression and classification in AD diagnosis," NeuroImage, vol. 100, pp. 91-105, Oct. 2014.
43. B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani, "Least angle regression," Ann. Statist., vol. 32, no. 2, pp. 407-499, 2004.
44. X. Zhu, Z. Huang, H. T. Shen, and X. Zhao, "Linear cross-modal hashing for efficient multimedia search," in Proc. 21st ACM Int. Conf. Multimedia, 2013, pp. 143-152.
45. H. Zou, T. Hastie, and R. Tibshirani, "Sparse principal component analysis," J. Comput. Graph. Statist., vol. 15, no. 2, pp. 265-286, Jun. 2006.
46. X. Zhu, Z. Huang, H. Cheng, J. Cui, and H. T. Shen, "Sparse hashing for fast multimedia search," ACM Trans. Inf. Syst., vol. 31, no. 2, 2013, Art. ID 9.
47. A. Argyriou, T. Evgeniou, and M. Pontil, "Convex multi-task feature learning," Mach. Learn., vol. 73, no. 3, pp. 243-272, 2008.
48. B. A. Olshausen and D. J. Field, "Emergence of simple-cell receptive field properties by learning a sparse code for natural images," Nature, vol. 381, no. 6583, pp. 607-609, 1996.
49. X. Zhu, Z. Huang, J. Cui, and H. T. Shen, "Video-to-shot tag propagation by graph sparse group lasso," IEEE Trans. Multimedia, vol. 15, no. 3, pp. 633-646, Apr. 2013.
50. J. Peng et al., "Regularized multivariate regression for identifying master predictors with application to integrative genomics study of breast cancer," Ann. Appl. Statist., vol. 4, no. 1, pp. 53-77, 2010.
51. P. Sprechmann, I. Ramírez, G. Sapiro, and Y. C. Eldar, "C-HiLasso: A collaborative hierarchical sparse modeling framework," IEEE Trans. Signal Process., vol. 59, no. 9, pp. 4183-4198, Sep. 2011.
52. M. Tan, L. Wang, and I. W. Tsang, "Learning sparse SVM for feature selection on very high dimensional datasets," in Proc. 27th ICML, 2010, pp. 1047-1054.
53. 1,∞ regularization," in Proc. 26th Annu. ICML, 2009, pp. 857-864.
54. X. Zhu, X. Li, and S. Zhang, "Block-row sparse multiview multilabel learning for image classification," IEEE Trans. Cybern., vol. 46, no. 2, pp. 450-461, Feb. 2015.
55. Y. Yang, Z. Ma, A. G. Hauptmann, and N. Sebe, "Feature selection for multimedia analysis by sharing information among multiple tasks," IEEE Trans. Multimedia, vol. 15, no. 3, pp. 661-669, Apr. 2013.
56. X. Zhu, Z. Huang, Y. Yang, H. T. Shen, C. Xu, and J. Luo, "Selftaught dimensionality reduction on the high-dimensional small-sized data," Pattern Recognit., vol. 46, no. 1, pp. 215-229, 2013.
57. 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. CVPR, Jun. 2014, pp. 3089-3096.
58. M. Belkin, P. Niyogi, and V. Sindhwani, "Manifold regularization: A geometric framework for learning from labeled and unlabeled examples," J. Mach. Learn. Res., vol. 7, pp. 2399-2434, Nov. 2006.
59. 2 methods for finding sparse solutions," IEEE J. Sel. Topics Signal Process., vol. 4, no. 2, pp. 317-329, Apr. 2010.
60. 2,1-norms minimization," in Proc. NIPS, 2010, pp. 1813-1821.
61. S. Negahban, P. Ravikumar, M. J. Wainwright, and B. Yu, "A unified framework for high-dimensional analysis of M-estimators with decomposable regularizers," Statist. Sci., vol. 27, no. 4, pp. 538-557, 2012.
62. R. Raina, A. Battle, H. Lee, B. Packer, and A. Y. Ng, "Self-taught learning: Transfer learning from unlabeled data," in Proc. 24th ICML, 2007, pp. 759-766.