P. Drineas, A. Frieze, R. Kannan, S. Vempala, and V. Vinay, Clustering large graphs via the singular value decomposition, Mach. Learn, vol.56, issue.1-3, pp.9-33, 2004.

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.

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

N. Keriven, A. Bourrier, R. Gribonval, and P. Pérez, Sketching for large-scale learning of mixture models, Inform. Inference, vol.7, issue.3, pp.447-508, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01208027

N. Keriven, N. Tremblay, Y. Traonmilin, and R. Gribonval, Compressive K-means, Proc. IEEE Int. Conf. Acoust. Speech & Signal Process, pp.6369-6373, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01386077

R. Gribonval, G. Blanchard, N. Keriven, and Y. Traonmilin, Compressive statistical learning with random feature moments, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01544609

A. Feuerverger and R. A. Mureika, The empirical characteristic function and its applications, Ann. Statist, vol.5, issue.1, pp.88-97, 1977.

Y. C. Pati, R. Rezaiifar, and P. S. Krishnaprasad, Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition, Proc. Asilomar Conf. Signals Syst. Comput, pp.40-44, 1993.

E. Byrne, R. Gribonval, and P. Schniter, Sketched clustering via hybrid approximate message passing, Proc. Asilomar Conf. Signals Syst. Comput, pp.410-414, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01650160

P. Mccullagh and J. A. Nelder, Generalized Linear Models, 1989.

A. Papoulis, Probability, Random Variables, and Stochastic Processes, 1991.

M. Rudelson and R. Vershynin, Hanson-Wright inequality and subGaussian concentration, Electron. Commun. Probab, vol.18, issue.82, pp.1-9, 2013.

E. M. Byrne and P. Schniter, Sparse multinomial logistic regression via approximate message passing, IEEE Trans. Signal Process, vol.64, issue.21, pp.5485-5498, 2016.

D. L. Donoho, A. Maleki, and A. Montanari, Message passing algorithms for compressed sensing, Proc. Nat. Acad. Sci, vol.106, pp.18-914, 2009.

S. Rangan, Generalized approximate message passing for estimation with random linear mixing, Proc. IEEE Int. Symp. Inform. Thy, pp.2168-2172, 2011.

M. Bayati and A. Montanari, The dynamics of message passing on dense graphs, with applications to compressed sensing, IEEE Trans. Inform. Theory, vol.57, issue.2, pp.764-785, 2011.

S. Rangan, A. K. Fletcher, V. K. Goyal, E. Byrne, and P. Schniter, Hybrid approximate message passing, IEEE Trans. Signal Process, vol.65, issue.17, pp.4577-4592, 2017.

R. Gatto and S. R. Jammalamadaka, The generalized von Mises distribution, Stat. Method, vol.4, pp.341-353, 2007.

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

J. P. Vila and P. Schniter, Expectation-maximization Gaussian-mixture approximate message passing, IEEE Trans. Signal Process, vol.61, issue.19, pp.4658-4672, 2013.

D. Bertsekas, Nonlinear Programming, 1999.

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

H. W. Kuhn, The Hungarian Method for the Assignment Problem, Naval Research Logistics Quarterly, pp.83-97, 1955.

A. Y. Ng, M. I. Jordan, and Y. Weiss, On spectral clustering: Analysis and an algorithm, Proc. Neural Inform. Process. Syst. Conf, pp.849-856, 2001.

A. Vedaldi and B. Fulkerson, VLFeat: An open and portable library of computer vision algorithms, Proc. ACM Intl. Conf. Multimedia, 2010, pp.1469-1472

M. Muja and D. G. Lowe, Fast approximate nearest neighbors with automatic algorithm configuration, Proc. Intl. Conf. Comp. Vision Thy. Appl, pp.331-340, 2009.

Y. Traonmilin, N. Keriven, R. Gribonval, and G. Blanchard, Spikes super-resolution with random Fourier sampling, Proc. Workshop Signal Process. Adapt. Sparse Struct. Repr. (SPARS, pp.1-2, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01509863

R. G. Chatalic and N. Keriven, Large-scale high-dimensional clustering with fast sketching, Proc. IEEE Int. Conf. Acoust. Speech & Signal Process, pp.4714-4718, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01701121

V. Schellekens and L. Jacques, Quantized compressive k-means, IEEE Signal Process. Lett, vol.25, issue.8, p.4, 2018.

. Estim, , vol.64, p.0

. Tune, , vol.21, p.2

. Linear, , vol.7, p.4