Compressed sensing and robust recovery of low rank matrices

Conference Record - Asilomar Conference on Signals, Systems and Computers

Volumn , Issue , 2008, Pages 1043-1047

  Fazel, M ^a   Candes E ^b   Recht, B ^b   Parrilo, P ^c  

^a UNIVERSITY OF WASHINGTON   (United States)

^b CALIFORNIA INSTITUTE OF TECHNOLOGY   (United States)

^c MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Compressed sensing; Matrix rank minimization; Singular value decomposition

Indexed keywords

COLUMN SPACE; COMPRESSED SENSING; LOW-RANK MATRICES; MATRIX; MATRIX RANK MINIMIZATION; NOISY MEASUREMENTS; NOISY OBSERVATIONS; RECOVERY SCHEME; ROBUSTNESS ISSUES;

SIGNAL RECONSTRUCTION;

SINGULAR VALUE DECOMPOSITION;

EID: 70349857921     PISSN: 10586393     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ACSSC.2008.5074571     Document Type: Conference Paper

References (15)

1. D. Achlioptas and F. McSherry. Fast computation of low-rank approximations. Journal of the ACM.
2. S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press, 2003.
3. E. J. Candès. Compressive sampling. In International Congress of Mathematicians. Vol. III, pages 1433-1452. Eur. Math. Soc., Zürich, 2006.
4. E. J. Candès, J. Romberg, and T. Tao. Stable signal recovery from incomplete and inaccurate measurements. Comm. Pure Appl. Math., 59(8):1207-1223, Aug. 2006.
5. E. J. Candès and T. Tao. Decoding by linear programming. IEEE Transactions on Information Theory, 51(12):4203-4215, 2005.
6. A. Deshpande and S. Vempala. Adaptive sampling and fast lowrank matrix approximation. In Proc. Workshop on Randomization and Computation (RANDOM), 2006.
7. P. Drineas, M. W. Mahoney, and R. Kannan. Fast Monte Carlo algorithms for matrices II: Computing a low rank approximation to a matrix. SIAM Journal on computing, 36:158-183, 2006.
8. M. Fazel, E. Candès, B. Recht, and P. A. Parrilo. Compressed sensing of low-rank matrices. In preparation.
9. G. H. Golub and C. F. V. Loan. Matrix Computations. Johns Hopkins University Press, 1996.
10. S. Har-Peled. Low rank matrix approximation in linear time. Manuscript, 2006.
11. E. J. C. J-F Cai and Z. Shen. A singular value thresholding algorithm for matrix completion. Submitted for publication, 2008.
12. B. Recht, M. Fazel, and P. A. Parrilo. Guaranteed minimum rank solutions to linear matrix equations via nuclear norm minimization. 2007. Submitted to SIAM Review.
13. M. Rudelson and R. Vershynin. Sampling from large matrices: an approach through geometric functional analysis. Journal of the ACM.
14. T. Sarlos. Improved approximation algorithms for large matrices via random projections. In Proc. FOCS 2006, Symposium on Foundations of Computer Science, pages 143-152, Oct. 2006.
15. F. Woolfe, E. Liberty, V. Rokhlin, and M. Tygert. A fast randomized algorithm for the approximation of matrices. Appl. Comput. Harmon. Anal., 25:335-366, 2008.