A simplified low rank and sparse graph for semi-supervised learning

Neurocomputing

Volumn 140, Issue , 2014, Pages 84-96

  Zhao, Miaoyun ^a   Jiao, Licheng ^a   Feng, Jie ^a   Liu, Tianyu ^a  

^a XIDIAN UNIVERSITY   (China)

Author keywords

Joint sparse representation; Low rank representation; Sparse congruency representation; Sparse representation

Indexed keywords

ALGORITHMS;

COEFFICIENT MATRIX; GLOBAL STRUCTURE; LOW-RANK PROPERTIES; LOW-RANK REPRESENTATIONS; SEMI-SUPERVISED CLASSIFICATION; SEMI-SUPERVISED LEARNING; SPARSE CONGRUENCY REPRESENTATION; SPARSE REPRESENTATION;

SUPERVISED LEARNING;

EID: 84901491679     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2014.03.033     Document Type: Article

References (44)

1. X. Zhu, Semi-Supervised Learning Literature Survey, Computer Science Technical Report 1530, University of Wisconsin-Madison, vol. 2, 2005, 3.
2. Mallapragada P.K., Jin R., Jain A.K., Liu Y. Semiboost. boosting for semi-supervised learning. IEEE Trans. Pattern Anal. Mach. Intell. 2009, 31(11):2000-2014.
3. Belkin M., Niyogi P. Laplacian eigenmaps for dimensionality reduction and data representation. Neurocomputing 2003, 15(6):1373-1396.
4. Wu F., Wang W., Yang Y., Zhuang Y., Nie F. Classification by semi-supervised discriminative regularization. Neurocomputing 2010, 73(10-12):1641-1651.
5. Zhang H., Yu J., Wang M., Liu Y. Semi-supervised distance metric learning based on local linear regression for data clustering. Neurocomputing 2012, 93:100-105.
6. Sun S., Shawe-Taylor J. Sparse semi-supervised learning using conjugate functions. J. Mach. Learn. Res. 2010, 11:2423-2455.
7. S. Sun, Multi-view Laplacian support vector machines, in: Lecture Notes in Artificial Intelligence, vol. 7121, Springer, Beijing, China. 2011, pp. 209-222.
8. Culp M., Michailidis G. Graph-based semisupervised learning. IEEE Trans. Pattern Anal. Mach. Intell. 2008, 30(1):174-179.
9. Belkin M., Niyogi P., Sindhwani V. Manifold regularization. a geometric framework for learning from labeled and unlabeled examples. J. Mach. Learn. Res. 2006, 7:2399-2434.
10. X. Zhu, Z. Ghahramani, J. Lafferty, et al., Semi-supervised learning using Gaussian fields and harmonic functions, in: International Conference on Machine Learning, vol. 3, 2003, pp. 912-919.
11. Zhou D., Bousquet O., Lal T.N., Weston J., Schölkopf B. Learning with local and global consistency. Adv. Neural Inf. Process. Syst. 2004, 16(16):321-328.
12. Chen K., Wang S. Semi-supervised learning via regularized boosting working on multiple semi-supervised assumptions. IEEE Trans. Pattern Anal. Mach. Intell. 2011, 33(1):129-143.
13. Wang J., Wang F., Zhang C., Shen H.C., Quan L. Linear neighborhood propagation and its applications. IEEE Trans. Pattern Anal. Mach. Intel. 2009, 31(9):1600-1615.
14. W. Liu, S.-F. Chang, Robust multi-class transductive learning with graphs, in: IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Miami, Florida, USA. 2009, pp. 381-388.
15. T. Jebara, J. Wang, S.-F. Chang, Graph construction and b-matching for semi-supervised learning, in: Proceedings of the 26th Annual International Conference on Machine Learning, ACM, Montreal, Quebec, Canada. 2009, pp. 441-448.
16. Liu G., Lin Z., Yan S., Sun J., Yu Y., Ma Y. Robust recovery of subspace structures by low-rank representation. IEEE Trans. Pattern Anal. Mach. Intell. 2013, 171-184.
17. Aldroubi A. A review of subspace segmentation. problem, nonlinear approximations and applications, to motion segmentation. ISRN Signal Process. 2013, 2013:1-13.
18. Ma Y., Yang A.Y., Derksen H., Fossum R. Estimation of subspace arrangements with applications in modeling and segmenting mixed data. SIAM Rev. 2008, 50(3):413-458.
19. Rao S.R., Yang A.Y., Sastry S.S., Ma Y. Robust algebraic segmentation of mixed rigid-body and planar motions from two views. Int. J. Comput. Vis. 2010, 88(3):425-446.
20. Shi J., Malik J. Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 2000, 22(8):888-905.
21. E. Elhamifar, R. Vidal, Sparse subspace clustering, in: IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Miami, Florida, USA. 2009, pp. 2790-2797.
22. Chen G., Lerman G. Spectral curvature clustering (scc). Int. J. Comput. Vis. 2009, 81(3):317-330.
23. Wright J., Ma Y., Mairal J., Sapiro G., Huang T.S., Yan S. Sparse representation for computer vision and pattern recognition. Proceedings of the IEEE, Special Issue on Applications of Compressive Sensing & Sparse Representation 2010, vol. 98(6). pp. 1031-1044.
24. A. Gittens, M.W. Mahoney, Revisiting the nyström method for improved large-scale machine learning, in: International Conference on Machine Learning, 2013.
25. S. Yan, H. Wang, Semi-supervised learning by sparse representation, in: SIAM International Conference on Data Mining, 2009, pp. 792-801.
26. Cheng B., Yang J., Yan S., Fu Y., Huang T.S. Learning with l1graph for image analysis. IEEE Trans. Image Process. 2010, 19(4):858-866.
27. S. Sun, Z. Hussain, J. Shawe-Taylor, Manifold-preserving graph reduction for sparse semi-supervised learning, Neurocomputing. 124 (2014) 13-21. doi:10.1016/j.neucom.2012.08.070.
28. L. Zhuang, H. Gao, Z. Lin, Y. Ma, X. Zhang, N. Yu, Non-negative low rank and sparse graph for semi-supervised learning, in: IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Providence, RI USA, 2012, pp. 2328-2335.
29. Zhang H., Nasrabadi N.M., Zhang Y., Huang T.S. Multi-view automatic target recognition using joint sparse representation. IEEE Trans. Aerosp. Electron. Syst. 2012, 48:2481-2497.
30. Y. Yang, H.T. Shen, Z. Ma, Z. Huang, X. Zhou, L 2, 1-norm regularized discriminative feature selection for unsupervised learning, in: Proceedings of the Twenty-Second international joint conference on Artificial Intelligence, AAAI Press, Menlo Park, California, 2011, pp. 1589-1594.
31. Liua R., Haoa R., Sua Z. Mixture of manifolds clustering via low rank embedding. J. Inf. Comput. Sci. 2011, 8(5):725-737.
32. Lin Z., Liu R., Su Z. Linearized alternating direction method with adaptive penalty for low-rank representation. Neural Inf. Process. Syst. 2011, 2:612-620.
33. G. Liu, Z. Lin, Y. Yu, Robust subspace segmentation by low-rank representation, in: Proceedings of the 27th International Conference on Machine Learning, 2010, pp. 663-670.
34. Coifman R.R., Shkolnisky Y., Sigworth F.J., Singer A. Graph Laplacian tomography from unknown random projections. IEEE Trans. Image Process. 2008, 17(10):1891-1899.
35. D. Cai, X. He, Y. Hu, J. Han, T. Huang, Learning a spatially smooth subspace for face recognition, in: IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Minneapolis, Southern Minnesota, USA, 2007, pp. 1-7.
36. D. Cai, X. He, J. Han, Spectral regression for efficient regularized subspace learning, in: Proceedings of the 11th International Conference on Computer Vision, IEEE, Rio de Janeiro, Brazil. 2007, pp. 1-8.
37. D. Cai, X. Wang, X. He, Probabilistic dyadic data analysis with local and global consistency, in: Proceedings of the 26th Annual International Conference on Machine Learning, 2009, pp. 105-112.
38. S.A. Nene, S.K. Nayar, H. Murase, Columbia Object Image Library (coil-20), Technical Report CUS-005-96, Columbia University, USA, 1996.
39. S. Lazebnik, C. Schmid, J. Ponce, Beyond bags of features: spatial pyramid matching for recognizing natural scene categories, in: Computer Vision and Pattern Recognition, vol. 2, IEEE, New York, USA. 2006, pp. 2169-2178.
40. A. Krizhevsky, G. Hinton, Learning multiple layers of features from tiny images (Masters thesis), Department of Computer Science, University of Toronto, 2009.
41. Cai D., He X., Han J., Huang T.S. Graph regularized nonnegative matrix factorization for data representation. IEEE Trans. Pattern Anal. Mach. Intell. 2011, 33(8):1548-1560.
42. R. He, W.-S. Zheng, B.-G. Hu, X.-W. Kong, Nonnegative sparse coding for discriminative semi-supervised learning, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Colorado Springs, USA. 2011, pp. 2849-2856.
43. Yu G., Zhang G., Domeniconi C., Yu Z., You J. Semi-supervised classification based on random subspace dimensionality reduction. Pattern Recognit. 2012, 45(3):1119-1135.
44. Parsons L., Haque E., Liu H. Subspace clustering for high dimensional data. a review. ACM SIGKDD Explor. Newsl. 2004, 6(1):90-105.