K. Slavakis, G. B. Giannakis, and G. Mateos, Modeling and Optimization for Big Data Analytics: (Statistical) learning tools for our era of data deluge, IEEE Signal Processing Magazine, vol.31, issue.5, pp.18-31, 2014.
DOI : 10.1109/MSP.2014.2327238

S. Foucart and H. Rauhut, A Mathematical Introduction to Compressive Sensing, Applied and Numerical Harmonic Analysis
DOI : 10.1007/978-0-8176-4948-7

T. Blumensath, Sampling and Reconstructing Signals From a Union of Linear Subspaces, IEEE Transactions on Information Theory, vol.57, issue.7, pp.4660-4671, 2011.
DOI : 10.1109/TIT.2011.2146550

F. Bunea, A. B. Tsybakov, M. H. Wegkamp, and A. Barbu, SPADES and mixture models, The Annals of Statistics, vol.38, issue.4, pp.2525-2558, 2010.
DOI : 10.1214/09-AOS790

URL : https://hal.archives-ouvertes.fr/hal-00514124

K. Bertin, E. L. Pennec, and V. Rivoirard, Adaptive Dantzig density estimation Annales de l'Institut Henri Poincaré, Probabilités et Statistiques, pp.43-74, 2011.
DOI : 10.1214/09-aihp351

URL : http://arxiv.org/abs/0905.0884

A. Bourrier, R. Gribonval, and P. Pérez, Compressive Gaussian Mixture estimation, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp.6024-6028, 2013.
DOI : 10.1109/ICASSP.2013.6638821

URL : https://hal.archives-ouvertes.fr/hal-00799896

G. Cormode and S. Muthukrishnan, An improved data stream summary: the count-min sketch and its applications, Journal of Algorithms, vol.55, issue.1, pp.58-75, 2005.
DOI : 10.1016/j.jalgor.2003.12.001

G. Cormode and M. Hadjieleftheriou, Methods for finding frequent items in data streams, The VLDB Journal, vol.15, issue.5, pp.3-20, 2009.
DOI : 10.1007/s00778-009-0172-z

N. Thaper, S. Guha, P. Indyk, and N. Koudas, Dynamic multidimensional histograms, Proceedings of the 2002 ACM SIGMOD international conference on Management of data , SIGMOD '02, p.428, 2002.
DOI : 10.1145/564691.564741

D. Achlioptas, Database-friendly random projections, Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems , PODS '01, pp.274-281, 2001.
DOI : 10.1145/375551.375608

H. Reboredo, F. Renna, R. Calderbank, and M. D. Rodrigues, Compressive classification, 2013 IEEE International Symposium on Information Theory, p.5, 2013.
DOI : 10.1109/ISIT.2013.6620311

URL : http://arxiv.org/abs/1302.4660

. Oldaric-ambrym, R. Maillard, and . Munos, Compressed Least-Squares Regression, pp.1-9, 2009.

A. R. Hall, Generalized method of moments, 2005.

A. Bourrier, M. E. Davies, T. Peleg, P. Pérez, and R. Gribonval, Fundamental Performance Limits for Ideal Decoders in High-Dimensional Linear Inverse Problems, IEEE Transactions on Information Theory, vol.60, issue.12, pp.7928-7946, 2014.
DOI : 10.1109/TIT.2014.2364403

URL : https://hal.archives-ouvertes.fr/hal-00908358

Y. C. Pati, R. Rezaiifar, and P. S. Krishnaprasad, Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition, Proceedings of 27th Asilomar Conference on Signals, Systems and Computers, pp.40-44, 1993.
DOI : 10.1109/ACSSC.1993.342465

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

P. Jain, A. Tewari, I. S. Dhillon-douglas, A. Reynolds, T. F. Quatieri et al., Orthogonal matching pursuit with replacement Speaker Verification Using Adapted Gaussian Mixture Models, Advances in Neural Information Processing Systems, pp.1-9, 2000.

E. Candes, J. K. Romberg, and T. Tao, Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information, IEEE Transactions on Information Theory, vol.52, issue.2, pp.480-509, 2006.
DOI : 10.1109/TIT.2005.862083

URL : http://arxiv.org/abs/math/0409186

S. Dasgupta, Learning mixtures of Gaussians, 40th Annual Symposium on Foundations of Computer Science (Cat. No.99CB37039), 1999.
DOI : 10.1109/SFFCS.1999.814639

J. Haupt, R. Castro, and R. Nowak, Distilled Sensing: Adaptive Sampling for Sparse Detection and Estimation, IEEE Transactions on Information Theory, vol.57, issue.9, 2011.
DOI : 10.1109/TIT.2011.2162269

URL : http://arxiv.org/abs/1001.5311

A. Vedaldi and B. Fulkerson, VLFeat -An open and portable library of computer vision algorithms, pp.1-4, 2010.

O. Cappé and E. Moulines, On-line expectation-maximization algorithm for latent data models, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.11, issue.3, pp.593-613, 2009.
DOI : 10.1111/j.1467-9868.2009.00698.x

K. Bharath, A. Sriperumbudur, K. Gretton, B. Fukumizu, G. R. Schölkopf et al., Hilbert space embeddings and metrics on probability measures, The Journal of Machine Learning Research, vol.11, pp.1517-1561, 2010.

A. Rahimi and B. Recht, Random Features for Large Scale Kernel Machines, Advances in Neural Information Processing Systems, pp.1-8, 2007.

M. Seyed-omid-sadjadi, L. Slaney, and . Heck, Msr identity toolbox v1.0: A matlab toolbox for speakerrecognition research, Speech and Language Processing Technical Committee Newsletter, 2013.