B. Widrow, D. Samuel, and . Stearns, Adaptive signal processing, 1985.

L. Donald and . Duttweiler, Proportionate normalized leastmean-squares adaptation in echo cancelers, IEEE Trans. on Speech and Audio Processing, vol.8, issue.5, pp.508-518, 2000.

W. Schreiber, Advanced television systems for terrestrial broadcasting: Some problems and some proposed solutions, Proceedings of the IEEE, pp.958-981, 1995.
DOI : 10.1109/5.387095

Y. Gu, J. Jin, and S. Mei, norm constraint lms algorithm for sparse system identification, IEEE Signal Processing Letters, vol.16, issue.0 9, pp.774-777, 2009.

Y. Chen, Y. Gu, O. Alfred, and . Hero, Sparse lms for system identification, pp.3125-3128, 2009.

Y. Chen, Y. Gu, O. Alfred, and . Hero, Regularized least-mean-square algorithms, 2010.

T. Hu and D. B. Chklovskii, Sparse LMS via online linearized Bregman iteration, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012.
DOI : 10.1109/ICASSP.2014.6855000

L. Yu, J. Barbot, G. Zheng, and H. Sun, Bayesian compressive sensing for cluster structured sparse signals, Signal Processing, vol.92, issue.1, pp.259-269, 2012.
DOI : 10.1016/j.sigpro.2011.07.015
URL : https://hal.archives-ouvertes.fr/hal-00636853

Z. Zhang, D. Bhaskar, and . Rao, Sparse Signal Recovery With Temporally Correlated Source Vectors Using Sparse Bayesian Learning, IEEE Journal of Selected Topics in Signal Processing, vol.5, issue.5, pp.912-926, 2011.
DOI : 10.1109/JSTSP.2011.2159773

L. Yu, H. Sun, G. Zheng, and J. Barbot, Model based Bayesian compressive sensing via Local Beta Process, Signal Processing, vol.108, issue.3, pp.259-271, 2015.
DOI : 10.1016/j.sigpro.2014.09.018
URL : https://hal.archives-ouvertes.fr/hal-01094927

R. Christian, S. Berger, . Zhou, C. James, P. Preisig et al., Sparse channel estimation for multicarrier underwater acoustic communication: From subspace methods to compressed sensing, IEEE Trans. on Signal Processing, vol.58, issue.3, pp.1708-1721, 2010.

A. Gramfort, M. Kowalski, and M. Hämäläinen, Mixed-norm estimates for the M/EEG inverse problem using accelerated gradient methods, Physics in Medicine and Biology, vol.57, issue.7, 1937.
DOI : 10.1088/0031-9155/57/7/1937
URL : https://hal.archives-ouvertes.fr/hal-00690774

S. Shalev-shwartz, Online Learning, 2007.
DOI : 10.1017/CBO9781107298019.022

L. Patrick, J. Combettes, and . Pesquet, Proximal splitting methods in signal processing, " in Fixed-point algorithms for inverse problems in science and engineering, pp.185-212, 2011.

A. Gramfort, D. Strohmeier, J. Haueisen, M. S. Hämäläinen, and M. Kowalski, Time-frequency mixed-norm estimates: Sparse M/EEG imaging with non-stationary source activations, NeuroImage, vol.70, pp.410-422, 2013.
DOI : 10.1016/j.neuroimage.2012.12.051
URL : https://hal.archives-ouvertes.fr/hal-00773276