D. Achlioptas and F. Mcsherry, Fast computation of low-rank matrix approximations, Journal of the ACM, vol.54, issue.2, p.9, 2007.
DOI : 10.1145/1219092.1219097

S. Bhojanapalli, P. Jain, and S. Sanghavi, Tighter Low-rank Approximation via Sampling the Leveraged Element, Proceedings of the Twenty-Sixth Annual ACM- SIAM Symposium on Discrete Algorithms, pp.902-920, 2015.
DOI : 10.1137/1.9781611973730.62

L. Kenneth, . Clarkson, P. David, and . Woodruff, Numerical linear algebra in the streaming model, Proceedings of the forty-first annual ACM symposium on Theory of computing, pp.205-214, 2009.

L. Kenneth, . Clarkson, P. David, and . Woodruff, Low rank approximation and regression in input sparsity time, Proceedings of the forty-fifth annual ACM symposium on Theory of computing, pp.81-90, 2013.

M. Ghashami and J. M. Phillips, Relative Errors for Deterministic Low-Rank Matrix Approximations, SODA, pp.707-717, 2014.
DOI : 10.1137/1.9781611973402.53

N. Halko, . Per-gunnar-martinsson, and . Tropp, Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions, SIAM Review, vol.53, issue.2, pp.217-288, 2011.
DOI : 10.1137/090771806

E. Liberty, Simple and deterministic matrix sketching, Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '13, pp.581-588, 2013.
DOI : 10.1145/2487575.2487623

I. Mitliagkas, C. Caramanis, and P. Jain, Memory limited, streaming PCA, Advances in Neural Information Processing Systems, 2013.

A. Joel and . Tropp, Improved analysis of the subsampled randomized hadamard transform Advances in Adaptive Data Analysis, pp.115-126, 2011.

A. Joel and . Tropp, User-friendly tail bounds for sums of random matrices, Foundations of Computational Mathematics, vol.12, issue.4, pp.389-434, 2012.

D. Woodruff, Low rank approximation lower bounds in row-update streams, Advances in Neural Information Processing Systems, pp.1781-1789, 2014.