New restricted isometry results for noisy low-rank recovery

IEEE International Symposium on Information Theory - Proceedings

Volumn , Issue , 2010, Pages 1573-1577

  Mohan, Karthik ^a   Fazel, Maryam ^a  

^a UNIVERSITY OF WASHINGTON   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPRESSED SENSING; FUNDAMENTAL PROBLEM; LINEAR MEASUREMENTS; LOW-RANK MATRICES; MACHINE-LEARNING; NOISY MEASUREMENTS; TRACE MINIMIZATION;

INFORMATION THEORY; MATRIX ALGEBRA; OPTIMIZATION;

RECOVERY;

EID: 77955682700     PISSN: 21578103     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISIT.2010.5513471     Document Type: Conference Paper

References (19)

1. J.F. Cai, EJ. Candes, and Z. Shen. A singular value thresholding algorithm for matrix completion. 2009. Available at http://arxiv.org/abs/0810. 3286, Submitted on 18 Oct 2008.
2. T.T. Cai, G. Xu, and J. Zhang. New bounds for restricted isometry constants. 2009. Technical Report, Available Online.
3. 1 minimization. In Proceedings of the 2009 IEEE Transactions on Information Theory, pages 3388-3397, 2009.
4. T.T. Cai, G. Xu, and J. Zhang. Shifting inequality and recovery of sparse signals. 2009. To appear in IEEE Transactions on Signal Processing.
5. EJ. Candes. The restricted isometry property and its implications for compressed sensing. Academie des Sciences, 2008.
6. EJ. Candes and Y. Plan. Tight oracle bounds for low-rank matrix recovery from a minimal number of random measurements. 2009. Available at http://arxiv.org/abs/1001.0339.
7. EJ. Candes and T. Tao. Decoding by linear programming. IEEE Transactions on Information Theory, 2004.
8. 1 minimization. Journal of Fourier Analysis and Applications, 14:877-905, 2008.
9. M. Fazel, E J. Candes, B. Recht, and P. Parrilo. Compressed sensing and robust recovery of low rank matrices. In Proc. Asilomar Conference, 2009.
10. M. Fazel, H. Hindi, and S. Boyd. A rank minimization heuristic with application to minimum order system approximation. In Proc. American Control Conference, 2001.
11. M. Fazel, H. Hindi, and S. Boyd. Log-det heuristic for matrix rank minimization with applications to hankel and euclidean distance matrices. In Proc. American Control Conference, 2003.
12. q minimization for 0 < q < 1, 2009.
13. K. Lee and Y. Bresler. Guaranteed minimum rank approximation from linear observations by nuclear norm minimization with an ellipsoidal constraint. Available online at http://arxiv.org/abs/0903.4742 Submitted on 27 Mar 2009.
14. K. Lee and Y Bresler. Admira:atomic decomposition for minimum rank approximation. 2009. Available at http://arxiv.org/abs/0905.0044.
15. R. Meka, P. Jain, and I.S. Dhillon. Guaranteed rank minimization via singular value projection. Available at http://arxiv.org/abs/0909.5457 Submitted on 30 Sep, 2009.
16. J.K. Merikoski and R. Kumar. Inequalities for spreads of matrix sums and products. Applied Mathematics E-Notes, 4:150 - 159, 2004.
17. K. Mohan and M. Fazel. Reweighted nuclear norm minimization with application to system identification. In Proc. ACC 2010.
18. D. Needell. Noisy signal recovery via iterative reweighted l1-minimization. In Proc. Asilomar conference on Signals, Systems and Computers, 2009. Available at http://arxiv.org/abs/0904.3780.
19. B. Recht, M. Fazel, and P. A. Parrilo. Guaranteed minimum rank solutions to linear matrix equations via nuclear norm minimization. Accepted for publication, SIAM Review., 2007.