P. Drineas, A. Frieze, R. Kannan, S. Vempala, and V. Vinay, Clustering Large Graphs via the Singular Value Decomposition, Machine Learning, vol.56, issue.1-3, pp.9-33, 2004.
DOI : 10.1023/B:MACH.0000033113.59016.96

H. Steinhaus, Sur la division des corps matériels en parties, Bull. Acad. Polon. Sci, vol.4, issue.12, pp.801-804, 1956.

A. K. Jain, Data clustering: 50 years beyond K-means, Pattern Recognition Letters, vol.31, issue.8, pp.651-666, 2010.
DOI : 10.1016/j.patrec.2009.09.011

D. Arthur and S. Vassilvitskii, k-means++: The advantages of careful seeding, Proc. ACM-SIAM Symp. Discrete Alg, pp.1027-1035, 2007.

N. Keriven, A. Bourrier, R. Gribonval, and P. Perez, Sketching for large-scale learning of mixture models, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016.
DOI : 10.1109/ICASSP.2016.7472867

URL : https://hal.archives-ouvertes.fr/hal-01329195

N. Keriven, N. Tremblay, Y. Traonmilin, and R. Gribonval, Compressive K-means, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016.
DOI : 10.1109/ICASSP.2017.7953382

URL : https://hal.archives-ouvertes.fr/hal-01386077

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://www.isr.umd.edu/~krishna/images/pati_reza_psk.pdf

E. M. Byrne and P. Schniter, Sparse Multinomial Logistic Regression via Approximate Message Passing, IEEE Transactions on Signal Processing, vol.64, issue.21, pp.5485-5498, 2016.
DOI : 10.1109/TSP.2016.2593691

URL : http://arxiv.org/pdf/1509.04491

M. Rudelson and R. Vershynin, Hanson-Wright inequality and sub-gaussian concentration, Electronic Communications in Probability, vol.18, issue.0, pp.1-9, 2013.
DOI : 10.1214/ECP.v18-2865

URL : http://doi.org/10.1214/ecp.v18-2865

D. L. Donoho, A. Maleki, and A. Montanari, Message-passing algorithms for compressed sensing, Proc. Nat. Acad. Sci, pp.18914-18919, 2009.
DOI : 10.1080/14786437708235992

URL : http://www.pnas.org/content/106/45/18914.full.pdf

S. Rangan, Generalized approximate message passing for estimation with random linear mixing, 2011 IEEE International Symposium on Information Theory Proceedings, pp.2168-2172, 2011.
DOI : 10.1109/ISIT.2011.6033942

M. Bayati and A. Montanari, The Dynamics of Message Passing on Dense Graphs, with Applications to Compressed Sensing, IEEE Transactions on Information Theory, vol.57, issue.2, pp.764-785, 2011.
DOI : 10.1109/TIT.2010.2094817

S. Rangan, A. K. Fletcher, V. K. Goyal, E. Byrne, and P. Schniter, Hybrid Approximate Message Passing, IEEE Transactions on Signal Processing, vol.65, issue.17, pp.4577-4592, 2017.
DOI : 10.1109/TSP.2017.2713759

URL : http://arxiv.org/pdf/1610.03082

R. Gatto and S. R. Jammalamadaka, The generalized von Mises distribution, Statistical Methodology, vol.4, issue.3, pp.341-353, 2007.
DOI : 10.1016/j.stamet.2006.11.003

C. M. Bishop, Pattern Recognition and Machine Learning, 2007.

J. P. Vila and P. Schniter, Expectation-Maximization Gaussian-Mixture Approximate Message Passing, IEEE Transactions on Signal Processing, vol.61, issue.19, pp.4658-4672, 2013.
DOI : 10.1109/TSP.2013.2272287

N. Keriven, N. Tremblay, and R. Gribonval, SketchMLbox : a Matlab toolbox for large-scale learning of mixture models, 2016.