S. Boyd and L. Vandenberghe, Convex Optimization, 2004.

A. M. Bruckstein, M. Elad, and M. Zibulevsky, On the Uniqueness of Nonnegative Sparse Solutions to Underdetermined Systems of Equations, IEEE Transactions on Information Theory, vol.54, issue.11, pp.4813-4820, 2008.
DOI : 10.1109/TIT.2008.929920

E. Candès, M. Wakin, and S. Boyd, Enhancing sparsity by reweighted

R. Chartrand and W. Yin, Iteratively reweighted algorithms for compressive sensing, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, 2008.
DOI : 10.1109/ICASSP.2008.4518498

M. Davies and R. Gribonval, Restricted isometry constants where p sparse recovery can fail for 0 < p ? 1, 2008.
URL : https://hal.archives-ouvertes.fr/inria-00544759

I. Daubechies, R. Devore, M. Fornasier, and S. Gunturk, Iteratively re-weighted least squares minimization for sparse recovery, 2009.

D. 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

D. Donoho and M. Elad, Optimally sparse representation in general (nonorthogonal) dictionaries via ??1 minimization, Proc. National Academy of Sciences, pp.2197-2202, 2003.
DOI : 10.1073/pnas.0437847100

M. Fazel, H. Hindi, and S. Boyd, Log-det heuristic for matrix rank minimization with applications to Hankel and Euclidean distance matrices, Proceedings of the 2003 American Control Conference, 2003., pp.2156-2162, 2003.
DOI : 10.1109/ACC.2003.1243393

M. Figueiredo, Adaptive sparseness using Jeffreys prior, Advances in Neural Information Processing Systems 14, pp.697-704, 2002.

J. Fuchs, On Sparse Representations in Arbitrary Redundant Bases, IEEE Transactions on Information Theory, vol.50, issue.6, pp.1341-1344, 2004.
DOI : 10.1109/TIT.2004.828141

R. Gribonval and M. Nielsen, Sparse representations in unions of bases, IEEE Transactions on Information Theory, vol.49, issue.12, pp.3320-3325, 2003.
DOI : 10.1109/TIT.2003.820031
URL : https://hal.archives-ouvertes.fr/inria-00570057

D. Malioutov, M. , and A. Willsky, Optimal sparse representations in general overcomplete bases, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing, pp.793-796, 2004.
DOI : 10.1109/ICASSP.2004.1326377

R. Neal, Bayesian Learning for Neural Networks, 1996.
DOI : 10.1007/978-1-4612-0745-0

J. Palmer, D. Wipf, K. Kreutz-delgado, and B. Rao, Variational EM algorithms for non-Gaussian latent variable models, Advances in Neural Information Processing Systems 18, pp.1059-1066, 2006.

B. Rao, K. Engan, S. F. Cotter, J. Palmer, and K. Kreutz-delgado, Subset selection in noise based on diversity measure minimization, IEEE Transactions on Signal Processing, vol.51, issue.3
DOI : 10.1109/TSP.2002.808076

B. Rao and K. Kreutz-delgado, An affine scaling methodology for best basis selection, IEEE Transactions on Signal Processing, vol.47, issue.1, pp.187-200, 1999.
DOI : 10.1109/78.738251

M. Tipping, Sparse Bayesian learning and the relevance vector machine, J. Machine Learning Research, vol.1, pp.211-244, 2001.

J. Tropp, Greed is Good: Algorithmic Results for Sparse Approximation, IEEE Transactions on Information Theory, vol.50, issue.10, pp.2231-2242, 2004.
DOI : 10.1109/TIT.2004.834793

D. Wipf and S. Nagarajan, A new view of automatic relevance determination, Advancies in Neural Information Processing Systems, 2008.