J. Astola, K. Egiazarian, A. Foi, and V. Katkovnik, From Local Kernel to Nonlocal Multiple- Model Image Denoising, Int. J. Comput. Vision, vol.86, issue.1, pp.1-32, 2010.

J. Astola, K. Egiazarian, and V. Katkovnik, Adaptive Window Size Image De-noising Based on Intersection of Confidence Intervals (ICI) Rule, J. Math. Imaging Vis, vol.16, issue.3, pp.223-235, 2002.

C. Brunet and S. Loustau, Noisy quantization: theory and practise, 2014.

M. Chichignoud, Minimax and minimax adaptive estimation in multiplicative regression: locally Bayesian approach. Probab. Theory Related Fields, pp.543-586, 2012.

M. Chichignoud and S. Loustau, Adaptive Noisy Clustering, IEEE Transactions on Information Theory, vol.60, issue.11, pp.1-14, 2014.
DOI : 10.1109/TIT.2014.2356577
URL : https://hal.archives-ouvertes.fr/hal-00831672

M. Chichignoud and S. Loustau, Bandwidth selection in ERM using the gradient, 2014.

F. Comte and C. Lacour, Anisotropic adaptive kernel deconvolution Annales de l'Institut Henri Poincaré, Probabilités et Statistiques, pp.569-609, 2013.
DOI : 10.1214/11-aihp470
URL : http://projecteuclid.org/download/pdfview_1/euclid.aihp/1366117659

A. Goldenshluger and A. Nemirovski, On spatially adaptive estimation of nonparametric regression, Math. Methods Statist, vol.6, issue.2, pp.135-170, 1997.

A. Goldenshluger and O. Lepski, Bandwidth selection in kernel density estimation: Oracle inequalities and adaptive minimax optimality, The Annals of Statistics, vol.39, issue.3, pp.1608-1632, 2011.
DOI : 10.1214/11-AOS883
URL : https://hal.archives-ouvertes.fr/hal-01265258

S. Graf and H. Luschgy, Foundation of quantization for probability distributions, Lecture Notes in Mathematics, vol.1730, 2000.
DOI : 10.1007/BFb0103945

J. A. Hartigan, Classification and Clustering, Journal of Marketing Research, vol.18, issue.4, 1975.
DOI : 10.2307/3151350

V. Katkovnik, A new method for varying adaptive bandwidth selection, IEEE Transactions on Signal Processing, vol.47, issue.9, pp.2567-2571, 1999.
DOI : 10.1109/78.782208

. Ch, J. Kervrann, and . Boulanger, Optimal Spatial Adaptation for Patch-Based Image Denoising, IEEE, vol.15, issue.10, pp.2866-2878, 2006.

O. V. Lepski, E. Mammen, and V. G. Spokoiny, Optimal spatial adaptation to inhomogeneous smoothness: an approach based on kernel estimates with variable bandwidth selectors, Ann. Statist, vol.25, issue.3, pp.929-947, 1997.

O. V. Lepski, On a Problem of Adaptive Estimation in Gaussian White Noise. Theory of Probability and its Applications, pp.454-466, 1990.

C. Levrard, Fast rates for empirical vector quantization, Electronic Journal of Statistics, vol.7, issue.0, 2012.
DOI : 10.1214/13-EJS822
URL : https://hal.archives-ouvertes.fr/hal-00664068

S. Loustau, Inverse statistical learning, Electronic Journal of Statistics, vol.7, issue.0, pp.2065-2097, 2013.
DOI : 10.1214/13-EJS838

P. Mathé, The Lepskii principle revisited, Inverse Problems, vol.22, issue.3, pp.11-15, 2006.
DOI : 10.1088/0266-5611/22/3/L02

D. Pollard, Strong Consistency of $K$-Means Clustering, The Annals of Statistics, vol.9, issue.1, 1981.
DOI : 10.1214/aos/1176345339

D. Pollard, A central limit theorem for k-means clustering. The Annals of Probability, 1982.

V. Spokoiny and C. Vial, Parameter tuning in pointwise adaptation using a propagation approach, The Annals of Statistics, vol.37, issue.5B, pp.2783-2807, 2009.
DOI : 10.1214/08-AOS607
URL : https://hal.archives-ouvertes.fr/hal-00377888