C. Aggarwal, On k-anonymity and the curse of dimensionality, Proceedings of the 31st VLDB Conference, 2005.

A. Beck and M. Teboulle, A fast iterative shrinkagethresholding algorithm for linear inverse problems, SIAM journal on imaging sciences, vol.2, issue.1, pp.183-202, 2009.

S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, Distributed optimization and statistical learning via the alternating direction method of multipliers, Trends Machine Learning, vol.3, pp.1-122, 2011.

J. Candès, M. B. Wakin, and S. P. Boyd, Enhancing sparsity by reweighted l1 minimization, Journal of Fourier analysis and applications, 2008.

A. Chambolle and C. Dossal, On the convergence of the iterates of "fista, Journal of Optimization Theory and Applications, issue.166, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01060130

A. Chambolle and T. Pock, A first-order primal-dual algorithm for convex problems with applications to imaging, Journal of Mathematical Imaging and Vision, vol.40, issue.1, pp.120-145, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00490826

A. Chambolle and T. Pock, On the ergodic convergence rates of a first-order primal-dual algorithm, Math. Program, vol.159, issue.1-2, pp.253-287, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01151629

C. Chaux, J. Pesquet, P. , and N. , Nested iterative algorithms for convex constrained image recovery problems. SIAM, pp.730-762, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00621932

P. L. Combettes and J. Pesquet, Proximal splitting methods in signal processing. In Fixed-point algorithms for inverse problems in science and engineering, pp.185-212, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00643807

P. L. Combettes and V. R. Wajs, Signal recovery by proximal forward-backward splitting, Multiscale Modeling & Simulation, vol.4, issue.4, pp.1168-1200, 2005.
URL : https://hal.archives-ouvertes.fr/hal-00017649

L. Condat, F. De-la-torre, and T. Kanade, Fast projection onto the simplex and the l1 ball, ICML 06 Proceedings of the 23rd international conference on Machine learning, vol.158, pp.575-585, 2006.
URL : https://hal.archives-ouvertes.fr/hal-01056171

C. Ding and T. Li, Adaptive dimension reduction using discriminant analysis and k-means clustering, Proceedings of the 24th International Conference on Machine Learning, pp.521-528, 2007.

D. Donoho, Compressed sensing, IEEE Trans. Inf. Theor, vol.52, issue.4, pp.1289-1306, 2006.
URL : https://hal.archives-ouvertes.fr/inria-00369486

D. L. Donoho and M. Elad, Optimally sparse representation in general (nonorthogonal) dictionaries via 1 minimization, Proceedings of the National Academy of Sciences, vol.100, issue.5, pp.2197-2202, 2003.

J. Duchi, S. Shalev-shwartz, Y. Singer, C. , and T. , Efficient projections onto the l 1-ball for learning in high dimensions, Proceedings of the 25th international conference on Machine learning, pp.272-279, 2008.

E. Esser, X. Zhang, C. , and T. F. , A general framework for a class of first order primal-dual algorithms for convex optimization in imaging science, SIAM J. Imaging Sci, vol.3, issue.4, pp.1015-1046, 2010.

, Robust supervised classification and feature selection using a primal-dual method

D. Evanko, Method of the year 2013: Methods to sequence the dna and rna of single cells are poised to transform many areas of biology and medicine, Nature Methods, vol.11, 2014.

N. Flammarion, B. Palaniappan, and F. R. Bach, Robust discriminative clustering with sparse regularizers, Journal of Machine Learning Research, vol.18, issue.80, pp.1-50, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01357666

J. Friedman, T. Hastie, and R. Tibshirani, Regularization path for generalized linear models via coordinate descent, Journal of Statistical Software, vol.33, pp.1-122, 2010.

T. S. Furey, N. Cristianini, N. Duffy, D. W. Bednarski, M. Schummer et al., Support vector machine classification and validation of cancer tissue samples using microarray expression data, Bioinformatics, vol.16, issue.10, pp.906-914, 2000.

I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, Gene selection for cancer classification using support vector machines, Machine learning, vol.46, issue.1-3, pp.389-422, 2002.

I. Guyon, S. Gunn, M. Nikravesh, and L. Zadeh, Feature extraction, foundations and applications. studies in fuzziness and soft computing, 2017.

T. Hastie, S. Rosset, R. Tibshirani, and J. Zhu, The entire regularization path for the support vector machine, Journal of Machine Learning Research, vol.5, pp.1391-1415, 2004.

T. Hastie, R. Tibshirani, W. , and M. , Statistcal learning with sparsity: The lasso and generalizations, 2015.

L. Jacob, G. Obozinski, and J. Vert, Group lasso with overlap and graph lasso, Proceedings of the 26th International Conference on Machine Learning (ICML09), pp.353-360, 2009.

C. Li and H. Li, Network-constrained regularization and variable selection for analysis of genomic data, Bioinformatics, vol.24, issue.9, pp.1175-1182, 2008.

J. Li, K. Cheng, S. Wang, F. Morstatter, P. Trevino et al., Feature selection: A data perspective, ACM Computing Surveys, vol.50, 2016.

P. Lions and B. Mercier, Splitting algorithms for the sum of two nonlinear operators, SIAM Journal on Numerical Analysis, vol.16, issue.6, pp.964-979, 1979.

J. Liu and J. Ye, Moreau-yosida regularization for grouped tree structure learning, Advances in Neural Information Processing Systems 23

M. Liu and B. C. Vemuri, A robust and efficient doubly regularized metric learning approach, Proceedings of the 12th European Conference on Computer VisionVolume Part IV, ECCV'12, 2012.

J. Mairal and B. Yu, Complexity analysis of the lasso regularization path, Proceedings of the 29th International Conference on Machine Learning (ICML-12), pp.353-360, 2012.

J. Moreau, Proximité et dualité dans un espace hilbertien, Bull. Soc.Math. France, vol.93, pp.273-299, 1965.

S. Mosci, L. Rosasco, M. Santoro, A. Verri, and S. Villa, Solving structured sparsity regularization with proximal methods, Machine Learning and Knowledge Discovery in Databases, pp.418-433, 2010.

A. Y. Ng, Feature selection, l 1 vs. l 2 regularization, and rotational invariance, Proceedings of the twenty-first international conference on Machine learning, p.78, 2004.

F. Nie, H. Huang, C. Xiao, and C. H. Ding, Efficient and robust feature selection via joint l2,1-norms minimization, Advances in Neural Information Processing Systems, vol.23, pp.1813-1821, 2010.

D. O'connor and L. Vandenberghe, Primal-dual decomposition by operator splitting and applications to image deblurring, SIAM, vol.7, issue.3, pp.1724-1754, 2014.

T. Pock, D. Cremers, H. Bischof, C. , and A. , An algorithm for minimizing the mumford-shah functional, IEEE 12th International Conference on, pp.1133-1140, 2009.

M. Radovanovic, A. Nanopoulos, and M. Ivanovic, Hubs in space: Popular nearest neighbors in high-dimensional data, Journal of Machine Learning Research, vol.11, pp.2487-2531, 2010.

N. Schaum, Single-cell transcriptomics of 20 mouse organs creates a tabula muris, Nature, vol.562, issue.7727, pp.367-372, 2018.

N. Simon, J. Friedman, T. Hastie, and R. Tibshirani, A sparse-group lasso, Journal of Computational and Graphical Statistics, vol.22, issue.2, pp.231-245, 2013.

S. Sra, Scalable nonconvex inexact proximal splitting, Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems, pp.539-547, 2012.

R. Tibshirani, Robust supervised classification and feature selection using a primal-dual method, Journal of the Royal Statistical Society. Series B (Methodological), pp.267-288, 1996.

T. Valkonen and T. Pock, Acceleration of the PDHGM on partially strongly convex functions, J. Math. Imaging Vision, vol.59, issue.3, pp.394-414, 2017.

D. M. Witten and R. Tibshirani, A framework for feature selection in clustering, Journal of the American Statistical Association, vol.105, issue.490, pp.713-726, 2010.

M. Yuan and Y. Lin, Model selection and estimation in regression with grouped variables, J. R. Stat. Soc. Ser. B, vol.68, issue.1, pp.49-67

H. Zou and T. Hastie, Regularization and variable selection via the elastic net, Journal of the Royal Statistical Society, Series B, vol.67, pp.301-320, 2005.

H. Zou, T. Hastie, and R. Tibshirani, Sparse principal component analysis, Journal of computational and graphical statistics, vol.15, issue.2, pp.265-286, 2006.