Distributed low-rank subspace segmentation

Proceedings of the IEEE International Conference on Computer Vision

Volumn , Issue , 2013, Pages 3543-3550

  Talwalkar, Ameet ^a   MacKey, Lester ^b   Mu, Yadong ^c   Chang, Shih Fu ^c   Jordan, Michael I ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b STANFORD UNIVERSITY   (United States)

^c Columbia University ^*  (United States)

Author keywords

Distributed; Divide and conquer; Low rank methods; Scalable; Subspace Segmentation

Indexed keywords

FACE RECOGNITION;

DISTRIBUTED; DIVIDE AND CONQUER; LOW-RANK METHODS; SCALABLE; SUB-SPACE SEGMENTATION;

SUPERVISED LEARNING;

EID: 84898779264     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2013.440     Document Type: Conference Paper

References (32)

1. A. Adler, M. Elad, and Y. Hel-Or. Probabilistic subspace clustering via sparse representations. IEEE Signal Process. Lett., 20(1):63-66, 2013.
2. R. Basri and D. Jacobs. Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell., 25(3):218-233, 2003.
3. E. J. Cand̀es, X. Li, Y. Ma, and J. Wright. Robust principal component analysis? Journal of the ACM, 58(3):1-37, 2011.
4. B. Cheng, G. Liu, J.Wang, Z. Huang, and S. Yan. Multi-task low-rank affinity pursuit for image segmentation. In ICCV, 2011.
5. B. Cheng, J. Yang, S. Yan, Y. Fu, and T. S. Huang. Learning with l1-graph for image analysis. IEEE Transactions on Image Processing, 19(4):858-866, 2010.
6. J. Costeira and T. Kanade. A multibody factorization method for independently moving objects. International Journal of Computer Vision, 29(3), 1998.
7. E. Elhamifar and R. Vidal. Sparse subspace clustering. In CVPR, 2009.
8. B. R. Feng Niu, C. Ŕe, and S. J. Wright. Hogwild!: A lockfree approach to parallelizing stochastic gradient descent. In NIPS, 2011.
9. S. Gao, I. W. H. Tsang, L. T. Chia, and P. Zhao. Local features are not lonely-laplacian sparse coding for image classification. In CVPR, 2010.
10. C.W. Gear. Multibody grouping from motion images. Int. J. Comput. Vision, 29:133-150, August 1998.
11. R. Gemulla, E. Nijkamp, P. J. Haas, and Y. Sismanis. Largescale matrix factorization with distributed stochastic gradient descent. In KDD, 2011.
12. T. Hastie and P. Simard. Metrics and models for handwritten character recognition. Statistical Science, 13(1):54-65, 1998.
13. R. He, W.-S. Zheng, B.-G. Hu, and X.-W. Kong. Nonnegative sparse coding for discriminative semi-supervised learning. In CVPR, 2011.
14. W. Hoeffding. Probability inequalities for sums of bounded random variables. Journal of the American Statistical Association, 58(301):13-30, 1963.
15. S. Kumar, M. Mohri, and A. Talwalkar. On sampling-based approximate spectral decomposition. In ICML, 2009.
16. Z. Lin, A. Ganesh, J. Wright, L. Wu, M. Chen, and Y. Ma. Fast convex optimization algorithms for exact recovery of a corrupted low-rank matrix. UIUC Technical Report no UILUENG-09-2214, 2009.
17. G. Liu, Z. Lin, and Y. Yu. Robust subspace segmentation by low-rank representation. In ICML, 2010.
18. G. Liu, H. Xu, and S. Yan. Exact subspace segmentation and outlier detection by low-rank representation. arXiv: 1109.1646v2 [cs.IT], 2011.
19. G. Liu and S. Yan. Latent low-rank representation for subspace segmentation and feature extraction. In ICCV, 2011.
20. G. Liu and S. Yan. Active subspace: Toward scalable lowrank learning. Neural Computation, 24:3371-3394, 2012.
21. L. Mackey, A. Talwalkar, and M. I. Jordan. Divide-andconquer matrix factorization. In NIPS, 2011.
22. B. Recht. A simpler approach to matrix completion. arXiv:0910.0651v2 [cs.IT], 2009.
23. B. Recht and C. Ŕe. Parallel stochastic gradient algorithms for large-scale matrix completion. In Optimization Online, 2011.
24. C. Tomasi and T. Kanade. Shape and motion from image streams under orthography. International Journal of Computer Vision, 9(2):137-154, 1992.
25. L. Torresani, M. Szummer, and A. W. Fitzgibbon. Efficient object category recognition using classemes. In ECCV, 2010.
26. R. Vidal, Y. Ma, and S. Sastry. Generalized principal component analysis (gpca). IEEE Trans. Pattern Anal. Mach. Intell., 27(12):1-15, 2005.
27. F. Wang and C. Zhang. Label propagation through linear neighborhoods. In ICML, 2006.
28. J.Wang, F.Wang, C. Zhang, H. C. Shen, and L. Quan. Linear neighborhood propagation and its applications. IEEE Trans. Pattern Anal. Mach. Intell., 31(9):1600-1615, 2009.
29. 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):210-227, 2009.
30. A. Yang, J. Wright, Y. Ma, and S. Sastry. Unsupervised segmentation of natural images via lossy data compression. Computer Vision and Image Understanding, 110(2):212-225, 2008.
31. H.-F. Yu, C.-J. Hsieh, S. Si, and I. Dhillon. Scalable coordinate descent approaches to parallel matrix factorization for recommender systems. In ICDM, 2012.
32. L. Zhuang, H. Gao, Z. Lin, Y. Ma, X. Zhang, and N. Yu. Non-negative low rank and sparse graph for semi-supervised learning. In CVPR, 2012.