Recursive robust PCA or recursive sparse recovery in large but structured noise

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn , Issue , 2013, Pages 5954-5958

  Qiu, Chenlu ^a   Vaswani, Namrata ^a   Hogben, Leslie ^a  

^a IOWA STATE UNIVERSITY   (United States)

Author keywords

compressive sensing; robust PCA

Indexed keywords

COMPRESSIVE SENSING; HIGH PROBABILITY; LOW-DIMENSIONAL SUBSPACE; RECONSTRUCTION ERROR; ROBUST PCA; ROBUST PRINCIPAL COMPONENTS; SIGNAL-OF-INTEREST; STRUCTURED NOISE;

PRINCIPAL COMPONENT ANALYSIS; RECOVERY; SIGNAL PROCESSING; STATISTICS;

ACOUSTIC NOISE;

EID: 84890498882     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2013.6638807     Document Type: Conference Paper

References (26)

1. F. De La Torre and M. J. Black, "A framework for robust subspace learning", International Journal of Computer Vision, vol. 54, pp. 117-142, 2003.
2. E. J. Candes, X. Li, Y. Ma, and J. Wright, "Robust principal component analysis?", Journal of ACM, vol. 58, no. 3, 2011.
3. J. Wright and Y. Ma, "Dense error correction via l1-minimization", IEEE Trans. on Info. Th., vol. 56, no. 7, pp. 3540-3560, 2010.
4. V. Chandrasekaran, S. Sanghavi, P. A. Parrilo, and A. S. Willsky, "Rank-sparsity incoherence for matrix decomposition", SIAM Journal on Optimization, vol. 21, 2011.
5. S. Roweis, "Em algorithms for pca and spca", Advances in Neural Information Processing Systems, pp. 626-632, 1998.
6. T. Zhang and G. Lerman, "A novel m-estimator for robust pca", arXiv:1112.4863v1, 2011.
7. H. Xu, C. Caramanis, and S. Sanghavi, "Robust pca via outlier pursuit", IEEE Tran. on Information Theorey, vol. 58, no. 5, 2012.
8. M. McCoy and J. Tropp, "Two proposals for robust pca using semidefinite programming", arXiv:1012.1086v3, 2010.
9. M. Brand, "Incremental singular value decomposition of uncertain data with missing values", in European Conference on Computer Vision, 2002, pp. 707-720.
10. D. Skocaj and A. Leonardis, "Weighted and robust incremental method for subspace learning", in IEEE Intl. Conf. on Computer Vision (ICCV), Oct 2003, vol. 2, pp. 1494-1501.
11. Y. Li, L. Xu, J. Morphett, and R. Jacobs, "An integrated algorithm of incremental and robust pca", in IEEE Intl. Conf. Image Proc. (ICIP), 2003, pp. 245-248.
12. C. Qiu and N. Vaswani, "Real-time robust principal components pursuit", in Allerton Conference on Communication, Control, and Computing, 2010.
13. C. Qiu and N. Vaswani, "Support-predicted modified-cs for principal components pursuit", in IEEE Intl. Symp. on Information Theory (ISIT), 2011.
14. J.N. Laska, M.A. Davenport, and R.G. Baraniuk, "Exact signal recovery from sparsely corrupted measurements through the pursuit of justice", in Asilomar Conf. on Sig. Sys. Comp., Nov 2009, pp. 1556-1560.
15. N. H. Nguyen and T. D. Tran, "Robust lasso with missing and grossly corrupted observations", To appear in IEEE Transaction on Information Theory, 2012.
16. E. Candes and T. Tao, "Decoding by linear programming", IEEE Trans. Info. Th., vol. 51(12), pp. 4203-4215, Dec. 2005.
17. C. Qiu, N. Vaswani, and L. Hogben, "Recursive robust pca or recursive sparse recovery in large but structured noise", arXiv: 1211.3754 [cs.IT], 2012.
18. S. S. Chen, D. L. Donoho, and M. A. Saunders, "Atomic decomposition by basis pursuit", SIAM Journal on Scientific Computing, vol. 20, pp. 33-61, 1998.
19. E. Candes, J. Romberg, and T. Tao, "Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information", IEEE Trans. Info. Th., vol. 52(2), pp. 489-509, February 2006.
20. D. Donoho, "Compressed sensing", IEEE Trans. on Information Theory, vol. 52(4), pp. 1289-1306, April 2006.
21. E. Candes, "The restricted isometry property and its implications for compressed sensing", Compte Rendus de lAcademie des Sciences, Paris, Serie I, pp. 589-592, 2008.
22. E. Candes and T. Tao, "The dantzig selector: statistical estimation when p is much larger than n", Annals of Statistics, 2006.
23. Y. Jin and B. Rao, "Algorithms for robust linear regression by exploiting the connection to sparse signal recovery", in IEEE Intl. Conf. Acoustics, Speech, Sig. Proc. (ICASSP), 2010.
24. K. Mitra, A. Veeraraghavan, and R. Chellappa, "A robust regression using sparse learing for high dimensional parameter estimation problems", in IEEE Intl. Conf. Acous. Speech. Sig.Proc.(ICASSP), 2010.
25. C. Qiu and N. Vaswani, "Recursive sparse recovery in large but structured noise-part 2", arXiv: 1303.1144 [cs.IT], 2013.
26. J. A. Tropp, "User-friendly tail bounds for sums of random matrices", Foundations of Computational Mathematics, vol. 12, no. 4, 2012.