Joint hypergraph learning and sparse regression for feature selection

Pattern Recognition

Volumn 63, Issue , 2017, Pages 291-309

  Zhang, Zhihong ^a   Bai, Lu ^b   Liang, Yuanheng ^a   Hancock, Edwin ^c  

^a XIAMEN UNIVERSITY   (China)

^b CENTRAL UNIVERSITY OF FINANCE AND ECONOMICS   (China)

^c UNIVERSITY OF YORK   (United Kingdom)

Author keywords

Feature selection; Hypergraph learning; Sparse regression

Indexed keywords

GRAPH THEORY; GRAPHIC METHODS; MATRIX ALGEBRA; OPTIMIZATION;

FEATURE SELECTION METHODS; GRAPH-BASED FEATURES; HYPERGRAPH; NEIGHBORHOOD RELATION; NEIGHBORHOOD STRUCTURE; OPTIMIZATION ALGORITHMS; SPARSE REGRESSION; STATIC REPRESENTATION;

FEATURE EXTRACTION;

EID: 84998849867     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2016.06.009     Document Type: Article

References (45)

1. [1] Duda, R.O., Hart, P.E., Stork, D.G., Pattern Classification. 2012, John Wiley & Sons.
2. [2] Zhao, Z., Wang, L., Liu, H., Ye, J., On similarity preserving feature selection. IEEE Trans. Knowl. Data Eng. 25:3 (2013), 619–632.
3. [3] Peng, H., Long, F., Ding, C., 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.
4. [4] Sun, Y., Todorovic, S., Goodison, S., Local-learning-based feature selection for high-dimensional data analysis. IEEE Trans. Pattern Anal. Mach. Intell. 32:9 (2010), 1610–1626.
5. 2,1-norms minimization, in: Advances in Neural Information Processing Systems, 2010, pp. 1813–1821.
6. [6] F. Nie, S. Xiang, Y. Jia, C. Zhang, S. Yan, Trace ratio criterion for feature selection, in: AAAI, vol. 2, 2008, pp. 671–676.
7. [7] X. He, D. Cai, P. Niyogi, Laplacian score for feature selection, in: Advances in Neural Information Processing Systems, 2005, pp. 507–514.
8. [8] Z. Zhao, H. Liu, Spectral feature selection for supervised and unsupervised learning, in: Proceedings of the 24th International Conference on Machine Learning, ACM, 2007, pp. 1151–1157.
9. [9] D. Cai, C. Zhang, X. He, Unsupervised feature selection for multi-cluster data, in: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2010, pp. 333–342.
10. [10] Hou, C., Nie, F., Li, X., Yi, D., Wu, Y., Joint embedding learning and sparse regression: a framework for unsupervised feature selection. IEEE Trans. Cybern. 44:6 (2014), 793–804.
11. [11] Xu, Z., King, I., Lyu, M.-T., Jin, R., Discriminative semi-supervised feature selection via manifold regularization. IEEE Trans. Neural Netw. 21:7 (2010), 1033–1047.
12. [12] Zhao, J., Lu, K., He, X., Locality sensitive semi-supervised feature selection. Neurocomputing 71:10 (2008), 1842–1849.
13. [13] Z. Zhao, H. Liu, Semi-supervised feature selection via spectral analysis, in: SDM, SIAM, 2007, pp. 641–646.
14. [14] Liu, Y., Nie, F., Wu, J., Chen, L., Efficient semi-supervised feature selection with noise insensitive trace ratio criterion. Neurocomputing 105 (2013), 12–18.
15. [15] X. Niyogi, Locality preserving projections, in: Neural Information Processing Systems, vol. 16, MIT, 2004, p. 153.
16. [16] Adini, Y., Moses, Y., Ullman, S., Face recognition: the problem of compensating for changes in illumination direction. IEEE Trans. Pattern Anal. Mach. Intell. 19:7 (1997), 721–732.
17. [17] D.W. Jacobs, P.N. Belhumeur, R. Basri, Comparing images under variable illumination, in: Proceedings IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, 1998, pp. 610–617.
18. [18] Belhumeur, P.N., Kriegman, D.J., What is the set of images of an object under all possible illumination conditions?. Int. J. Comput. Vis. 28:3 (1998), 245–260.
19. [19] Hagen, L., Kahng, A.B., New spectral methods for ratio cut partitioning and clustering. IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst. 11:9 (1992), 1074–1085.
20. [20] S. Agarwal, K. Branson, S. Belongie, Higher order learning with graphs, in: Proceedings of the 23rd international conference on Machine learning, ACM, 2006, pp. 17–24.
21. [21] D. Zhou, J. Huang, B. Schölkopf, Learning with hypergraphs: clustering, classification, and embedding, in: Advances in Neural Information Processing Systems, 2006, pp. 1601–1608.
22. [22] Gibson, D., Kleinberg, J., Raghavan, P., Clustering categorical data: an approach based on dynamical systems. VLDB J. 8:3–4 (2000), 222–236.
23. [23] S. Agarwal, J. Lim, L. Zelnik-Manor, P. Perona, D. Kriegman, S. Belongie, Beyond pairwise clustering, in: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, IEEE, 2005, pp. 838–845.
24. [24] L. Sun, S. Ji, J. Ye, Hypergraph spectral learning for multi-label classification, in: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2008, pp. 668–676.
25. [25] Chung, F.R., Spectral Graph Theory. 1997, American Mathematical Society.
26. [26] Huang, Y., Liu, Q., Lv, F., Gong, Y., Metaxas, D.N., Unsupervised image categorization by hypergraph partition. IEEE Trans. Pattern Anal. Mach. Intell. 33:6 (2011), 1266–1273.
27. [27] Belkin, M., Niyogi, P., Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput. 15:6 (2003), 1373–1396.
28. [28] C. Hou, F. Nie, D. Yi, Y. Wu, Feature selection via joint embedding learning and sparse regression, in: Proceedings of the International Joint Conference on Artificial Intelligence, vol. 22, Citeseer, 2011, pp. 1324–1330.
29. [29] Zien, J.Y., Schlag, M.D., Chan, P.K., Multilevel spectral hypergraph partitioning with arbitrary vertex sizes. IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst. 18:9 (1999), 1389–1399.
30. [30] Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y., Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31:2 (2009), 210–227.
31. [31] Q. Gu, Z. Li, J. Han, Joint feature selection and subspace learning, in: Proceedings-International Joint Conference on Artificial Intelligence, vol. 22, 2011, pp. 1294–1299.
32. [32] Roweis, S.T., Saul, L.K., Nonlinear dimensionality reduction by locally linear embedding. Science 290:5500 (2000), 2323–2326.
33. [33] Belkin, M., Niyogi, P., Sindhwani, V., Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J. Mach. Learn. Res. 7 (2006), 2399–2434.
34. [34] Y. Huang, Q. Liu, D. Metaxas, Video object segmentation by hypergraph cut, in: IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2009, pp. 1738–1745.
35. [35] Nutt, C.L., Mani, D., Betensky, R.A., Tamayo, P., Cairncross, J.G., Ladd, C., Pohl, U., Hartmann, C., McLaughlin, M.E., Batchelor, T.T., et al. Gene expression-based classification of malignant gliomas correlates better with survival than histological classification. Cancer Res. 63:7 (2003), 1602–1607.
36. [36] Spira, A., Beane, J.E., Shah, V., Steiling, K., Liu, G., Schembri, F., Gilman, S., Dumas, Y.-M., Calner, P., Sebastiani, P., et al. Airway epithelial gene expression in the diagnostic evaluation of smokers with suspect lung cancer. Nat. Med. 13:3 (2007), 361–366.
37. [37] S.A. Nene, S.K. Nayar, H. Murase, et al., Columbia Object Image Library (Coil-20), Technical Report CUCS-005-96, 1996.
38. [38] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., Gradient-based learning applied to document recognition. Proc. IEEE 86:11 (1998), 2278–2324.
39. [39] G. Griffin, A. Holub, P. Perona, Caltech-256 Object Category Dataset.
40. [40] S. Lazebnik, C. Schmid, J. Ponce, Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories, in: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, IEEE, 2006, pp. 2169–2178.
41. [41] Chang, C.-C., Lin, C-J, Libsvm: a library for support vector machines. ACM Trans. Intell. Syst. Technol., 23(3), 2011, 10.1145/1961189.1961199.
42. [42] X. Zhu, Z. Ghahramani, J. Lafferty, et al., Semi-supervised learning using Gaussian fields and harmonic functions, in: ICML, 2003, pp. 912–919.
43. [43] Zhu, X., Huang, Z., Cui, J., Shen, H.T., Video-to-shot tag propagation by graph sparse group lasso. IEEE Trans. Multimed. 15:3 (2013), 633–646.
44. [44] Zhu, X., Huang, Z., Yang, Y., Shen, H.T., Xu, C., Luo, J., Self-taught dimensionality reduction on the high-dimensional small-sized data. Pattern Recognit. 46:1 (2013), 215–229.
45. [45] X. Zhu, X. Wu, W. Ding, S. Zhang, Feature selection by joint graph sparse coding, in: SDM, SIAM, 2013, pp. 803–811.