D. L. Donoho, For most large underdetermined systems of linear equations the minimal ???1-norm solution is also the sparsest solution, Communications on Pure and Applied Mathematics, vol.50, issue.6, 2004.
DOI : 10.1002/cpa.20132

P. Bofill and M. Zibulevsky, Underdetermined blind source separation using sparse representations, Signal Processing, vol.81, issue.11, pp.2353-2362, 2001.
DOI : 10.1016/S0165-1684(01)00120-7

R. Gribonval and S. Lesage, A survey of sparse component analysis for blind source separation: principles, perspectives, and new challenges, Proceedings of ESANN'06, pp.323-330, 2006.
URL : https://hal.archives-ouvertes.fr/inria-00544897

D. L. Donoho, M. Elad, and V. 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

F. Movahedi, G. H. Mohimani, M. Babaie-zadeh, and C. Jutten, Estimating the mixing matrix in Sparse Component Analysis (SCA) based on partial k-dimensional subspace clustering, Neurocomputing, vol.71, issue.10-12
DOI : 10.1016/j.neucom.2007.07.035

URL : https://hal.archives-ouvertes.fr/hal-00272347

Y. Washizawa and A. Cichocki, On-Line K-PLANE Clustering Learning Algorithm for Sparse Comopnent Analysis, 2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings, pp.681-684, 2006.
DOI : 10.1109/ICASSP.2006.1661367

Y. Q. Li, A. Cichocki, and S. Amari, Analysis of Sparse Representation and Blind Source Separation, Neural Computation, vol.401, issue.21, pp.1193-1234, 2004.
DOI : 10.1162/089976601300014385

M. Zibulevsky and B. A. Pearlmutter, Blind Source Separation by Sparse Decomposition in a Signal Dictionary, Neural Computation, vol.1, issue.4, pp.863-882, 2001.
DOI : 10.1016/S0042-6989(97)00169-7

P. G. Georgiev, F. J. Theis, and A. Cichocki, Blind source separation and sparse component analysis for over-complete mixtures, Proceedinds of ICASSP'04, pp.493-496, 2004.

Y. Li, A. Cichocki, and S. Amari, Sparse component analysis for blind source separation with less sensors than sources, ICA2003, pp.89-94, 2003.

S. S. Chen, D. L. Donoho, and M. A. Saunders, Atomic Decomposition by Basis Pursuit, SIAM Journal on Scientific Computing, vol.20, issue.1, pp.33-61, 1999.
DOI : 10.1137/S1064827596304010

D. L. Donoho and X. Huo, Uncertainty principles and ideal atomic decomposition, IEEE Transactions on Information Theory, vol.47, issue.7, pp.2845-2862, 2001.
DOI : 10.1109/18.959265

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.161.9300

M. Elad and A. Bruckstein, A generalized uncertainty principle and sparse representation in pairs of bases, IEEE Transactions on Information Theory, vol.48, issue.9, pp.2558-2567, 2002.
DOI : 10.1109/TIT.2002.801410

E. Candes and J. Romberg, 1-Magic: Recovery of Sparse Signals via Convex Programming

S. Mallat and Z. Zhang, Matching pursuits with time-frequency dictionaries, IEEE Transactions on Signal Processing, vol.41, issue.12, pp.3397-3415, 1993.
DOI : 10.1109/78.258082

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.335.5769

S. Krstulovic and R. Gribonval, Mptk: Matching Pursuit Made Tractable, 2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings, 2006.
DOI : 10.1109/ICASSP.2006.1660699

URL : https://hal.archives-ouvertes.fr/inria-00544919