Learning low rank matrices from O(n) entries

46th Annual Allerton Conference on Communication, Control, and Computing

Volumn , Issue , 2008, Pages 1365-1372

  Keshavan, Raghunandan H ^a   Montanari, Andrea ^b   Oh, Sewoong ^a  

^a Stanford University   (United States)

^b STANFORD UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EXTENSIVE SIMULATIONS; LOW-RANK MATRICES; MATRIXES; R MATRICES; RANDOM MATRICES;

EID: 64549091526     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ALLERTON.2008.4797720     Document Type: Conference Paper

References (9)

1. I. M. Johnstone "High Dimensional Statistical Inference and Random Matrices", Proc. Intl. Congr. of Math., Madrid, July 2006
2. A. M.-C. So and Y. Ye, "Theory of semidefinite programming for sensor network localization", Math. Progr. Series B, 109 (2007), 267-385
3. "Netflix prize", http://www.netflixprize.com/
4. B. Recht, M. Fazel, and P. A. Parrilo, "Guaranteed minimum rank solutions to linear matrix equations via nuclear norm minimization", preprint (2007), submitted to SIAM Review.
5. E. J. Candés and B. Recht, "Exact Matrix Completion via Convex Optimization", preprint (2008), available at http://www.acm.caltech. edu/~emmanuel/papers/MatrixCompletion. pdf.
6. Z. Liu and L. Vandenberghe, "Interior-point method for nuclear norm approximation with application to system identification", preprint (2008), available at http://www.ee.ucla.edu/~vandenbe/publications/nucnrm.pdf.
7. T. Cover and J. A. Thomas, Elements of Information Theory, Wiley Interscience, New York, 1991
8. B. Selman, H. A. Kautz, and B. Cohen "Noise strategies for improving local search," in Proc. of AAAI-94, Seattle, WA.
9. R. H. Keshavan, A. Montanari and S. Oh, "Learning low-rank matrices from O (n) observations: Algorithms and phase transitions", in preparation.