D. Achlioptas and F. Mcsherry, Fast computation of low rank matrix approximations, Journal of the ACM, vol.54, pp.1-19, 2007.

D. Donoho, M. Elad, and V. N. Temlyakov, Stable recovery of sparse overcomplete representations in the presence of noise, IEEE Transactions on Information Theory, vol.52, issue.1, pp.6-18, 2006.
DOI : 10.1109/TIT.2005.860430

P. Drineas, R. Kannan, and M. W. Mahoney, Fast Monte Carlo Algorithms for Matrices II: Computing a Low-Rank Approximation to a Matrix, SIAM Journal on Computing, vol.36, issue.1, pp.158-183, 2006.
DOI : 10.1137/S0097539704442696

A. Frieze, R. Kannan, and S. Vempala, Fast monte-carlo algorithms for finding low-rank approximations, Journal of the ACM, vol.51, issue.6, pp.1025-1041, 2004.
DOI : 10.1145/1039488.1039494

A. Juditsky and A. Nemirovski, On verifiable sufficient conditions for sparse signal recovery via ? 1 minimization. ? to appear in Mathematical Programming Series B, Special Issue on Machine Learning. E-print: http://www.optimization-online.org, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00321775

N. H. Nguyen, T. T. Do, and T. D. Tran, A fast and efficient algorithm for low-rank approximation of a matrix, Proceedings of the 41st annual ACM symposium on Symposium on theory of computing, STOC '09, pp.215-224, 2009.
DOI : 10.1145/1536414.1536446