Accelerated low-rank visual recovery by random projection

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

Volumn , Issue , 2011, Pages 2609-2616

  Mu, Yadong ^a   Dong, Jian ^a   Yuan, Xiaotong ^a   Yan, Shuicheng ^a  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; RECOVERY; ROBUST CONTROL; SINGULAR VALUE DECOMPOSITION;

COMPUTATIONAL BURDEN; LARGE-SCALE DATASETS; LOW-RANK PROPERTIES; OPTIMIZATION PROBLEMS; QUANTITATIVE STUDY; RANDOM PROJECTIONS; ROBUST PRINCIPAL COMPONENT ANALYSIS; THEORETIC ANALYSIS;

PRINCIPAL COMPONENT ANALYSIS;

EID: 80052892123     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2011.5995369     Document Type: Conference Paper

References (20)

1. O. ambrym Maillard and R. Munos. Compressed least-squares regression. In NIPS, 2009. 2609, 2610
2. C. Boutsidis and P. Drineas. Random projections for the nonnegative least-squares problem. Linear Algebra and Its Applications, 431:760-771, 2009. 2609, 2610
3. J.-F. Cai, E. Candes, and Z. Shen. A singular value thresholding algorithm for matrix completion. SIAM Journal of Optimization, 20(4):1956-1982, 2010. 2611
4. X. Chen, Y. Mu, S. Yan, and T.-S. Chua. Efficient large-scale image annotation by probabilistic collaborative multi-label propagation. In ACM Multimedia, 2010. 2615
5. D. Forsyth and J. Ponce. Computer Vision: A Modern Approach. Prentice Hall Professional Technical Reference, 2002. 2609
6. R. Horn. Topics in matrix analysis. Cambridge University Press, New York, NY, USA, 1986. 2612
7. F. D. la Torre and M. Black. Robust principal component analysis for computer vision. In ICCV, 2001. 2609
8. Z. Lin, A. Ganesh, J. Wright, L. Wu, M. Chen, and Y. Ma. Fast convex optimization algorithms for exact recovery of a corrupted lowrank matrix. UIUC Technical Report UILU-ENG-09-2214, 2009. 2610, 2611, 2613
9. V. Rokhlin and M. Tygert. A fast randomized algorithm for overdetermined linear least-squares regression. Proceedings of the National Academy of Sciences, 105(36):13212-13217, 2008. 2610
10. Q. Shi, C. Shen, and H. Li. Rapid face recognition using hashing. In CVPR, 2010. 2610
11. N. Srebro. Learing with Matrix Factorizations. PhD thesis, Department of Computer Science, MIT, 2004. 2609, 2612
12. N. Srebro, J. Rennie, and T. Jaakkola. Maximum margin matrix factorizations. In NIPS, 2005. 2610
13. S. Vempala. The Random Projection Method. Dimacs Series in Discrete Math, 2005. 2612, 2613
14. A. Wagner, J. Wright, A. Ganesh, Z. Zhou, and Y. Ma. Towards a practical face recognition system: Robust registration and illumination by sparse representation. In CVPR, 2009. 2610
15. F. Wang and P. Li. Efficient nonnegative matrix factorization with random projections. In SDM, 2010. 2609, 2610
16. J. Wright, A. Ganesh, S. Rao, and Y. Ma. Robust principal component analysis: Exact recovery of corrupted low-rank matrices. In NIPS, 2009. 2609, 2610, 2614
17. J. Wright and Y. Ma. Dense error correction via l1-minimization. In ICASSP, 2009. 2610
18. J. Wright, A. Yang, A. Ganesh, S. Sastry, and Y. Ma. Robust face recognition via sparse representation. IEEE Transactions Pattern Analysis Machine Intelligence, 31(2):210-227, 2009. 2610
19. L. Wu, A. Ganesh, B. Shi, Y. Matsushita, Y. Wang, and Y. Ma. Robust photometric stereo via low-rank matrix completion and recovery. In ACCV, 2010. 2609
20. J. Yang, J. Wright, T. Huang, and Y. Ma. Image super-resolution as sparse representation of raw image patches. In CVPR, 2008. 2610