Fixed-rank representation for unsupervised visual learning

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2012, Pages 598-605

  Liu, Risheng ^a,c   Lin, Zhouchen ^b,d   De La Torre, Fernando ^c   Su, Zhixun ^a  

^a DALIAN UNIVERSITY OF TECHNOLOGY   (China)

^b Peking University   (China)

^c CARNEGIE MELLON UNIVERSITY   (United States)

^d MICROSOFT RESEARCH ASIA   (China)

Author keywords

[No Author keywords available]

Indexed keywords

CLOSED FORM; DATA SAMPLING; MATRIX FACTORIZATIONS; NON-TRIVIAL; NUMERICAL SOLVERS; REAL APPLICATIONS; REGULARIZER; SUBSPACE CLUSTERING; SYNTHETIC DATA; UNIFIED FRAMEWORK; VISUAL LEARNING;

CLUSTERING ALGORITHMS; FEATURE EXTRACTION;

COMPUTER VISION;

EID: 84866669326     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2012.6247726     Document Type: Conference Paper

References (34)

1. R. S. Cabral, F. De la Torre, J. P. Costeira, and A. Bernardino. Matrix completion for multi-label image classification. In NIPS, 2011. 1
2. E. Candès, X. Li, Y. Ma, and J. Wright. Robust principal component analysis? Journal of the ACM, 58(1):1-37, 2011. 1
3. J. Costeira and T. Kanade. A multibody factorization method for independently moving objcets. IJCV, 29(3):159-179, 1998. 1, 3
4. F. De la Torre. A least-squares framework for component analysis. IEEE Trans. on PAMI, 34(6):1041-1055, June 2012. 2, 4
5. E. Elhamifar and R. Vidal. Sparse subspace clustering. In CVPR, 2009. 1, 2
6. E. Elhamifar and R. Vidal. Clustering disjoint subspaces via sparse representation. In ICASSP, 2010. 1, 2
7. P. Favaro, R. Vidal, and A. Ravichandran. A closed form solution to robust subspace estimation and clustering. In CVPR, 2011. 1, 2
8. M. Fischler and R. Bolles. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6):381-395, 1981. 5
9. A. Fisher. The statistical utilization of multiple measurements. Annals of Eugenics, 8:376-386, 1938. 2
10. G. Griffin, A. Holub, and P. Perona. Caltech-256 object category dataset. Caltech Technical Report, 2007. 6
11. X. He, D. Cai, S. Yan, and H. Zhang. Neighborhood preserving embedding. In ICCV, 2005. 7
12. X. He and P. Niyogi. Locality preserving projections. In NIPS, 2003. 7
13. H. Hotelling. Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology, 24(6):417-441, 1933. 2, 4
14. H. Hotelling. Relations between two sets of variates. Biometrika, 28:321-377, 1936. 2
15. Z. Lin, M. Chen, L. Wu, and Y. Ma. The augmented Lagrange multiplier method for exact recovery of corrupted low-rank matrices. UIUC Technical Report UILU-ENG-09-2215, 2009. 2, 4
16. Z. Lin, R. Liu, and Z. Su. Linearized alternating direction method with adaptive penalty for low rank representation. In NIPS, 2011. 2, 6
17. G. Liu, Z. Lin, S. Yan, J. Sun, Y. Yu, and Y. Ma. Robust recovery of subspace structures by low-rank representation. submitted to IEEE Tran. on PAMI, 2011. 1, 3, 7
18. G. Liu, Z. Lin, and Y. Yu. Robust subspace segmentation by low-rank representation. In ICML, 2010. 1, 2, 5
19. G. Liu, H. Xu, and S. Yan. Exact subspace segmentation and outlier detection by low-rank representation. submitted to JMLR (arXiv:1109.1646), 2011. 4
20. G. Liu and S. Yan. Latent low-rank representation for subspace segmentation and feature extraction. In ICCV, 2011. 1, 2
21. J. Liu, S. Ji, and J. Ye. Multi-task feature learning via efficient l2;1-norm minimization. In UAI, 2009. 2
22. R. Liu, Z. Lin, F. De la Torre, and Z. Su. Fixed-rank representation for unsupervised visual learning. Technical Report (arXiv:1203.2210), 2012. 3
23. B. Nasihatkon and R. Hartley. Graph connectivity in sparse subspace clustering. In CVPR, 2011. 1
24. Y. Ni, J. Sun, X. Yuan, S. Yan, and L. Cheong. Robust low-rank subspace segmentation with semidefinite guarantees. In ICDM Workshop, 2010. 1, 2
25. K. Pearson. On lines and planes of closest fit to systems of points in space. The London, Edinburgh and Dublin Philosophical Magazine and Journal, 6:559-572, 1901. 2, 4
26. P. Philips, P. Flynn, T. Scruggs, and K. Bowyer. Overview of the face recognition grand challenge. In CVPR, 2005. 6
27. S. Rao, R.Tron, R. Vidal, and Y. Ma. Motion segmentation in the presence of outlying, incomplete, and corrupted trajectories. IEEE Trans. on PAMI, 32(10):1832-1845, 2010. 1, 6
28. B. Recht, M. Fazel, and P. Parrilo. Guaranteed minimum-rank solutions of linear matrix equations via nuclear norm minimization. SIAM Review, 52(3):471-501, 2010. 1, 2, 3
29. Y. Shen, Z. Wen, and Y. Zhang. Augmented Lagrangian alternating direction method for matrix separation based on low-rank factorization. preprint, 2011. 5
30. J. Shi and J. Malik. Normalized cuts and image segmentation. IEEE Trans. on PAMI, 22(8):888-905, 2000. 1
31. M. Soltanolkotabi and E. Candès. A geometric analysis of subspace clustering with outliers. Technical Report (arXiv:1112.4258), 2011. 1, 2
32. R. Tron and R. Vidal. A benchmark for the comparison of 3D montion segmentation algorithms. In CVPR, 2007. 6
33. J. Wright, A. Yang, A. Ganesh, S. Sastry, and Y. Ma. Robust face recognition via sparse representation. IEEE Trans. on PAMI, 31(2):210-227, 2009. 1
34. J. Yan and M. Pollefeys. A general framework for montion segmentation: Independent, articulated, rigid, non-rigid, degenerate and nondegenerate. In ECCV, 2006. 5