F. Anouar, F. Badran, and S. Thiria, Probabilistic self-organizing map and radial basis function networks, Neurocomputing, vol.20, issue.1-3, pp.83-96, 1998.
DOI : 10.1016/S0925-2312(98)00026-5

C. M. Bishop, M. Svensén, and C. K. Williams, Developments of the generative topographic mapping, Neurocomputing, vol.21, issue.1-3, pp.203-224, 1998.
DOI : 10.1016/S0925-2312(98)00043-5

C. M. Bishop, M. Svensén, and C. K. Williams, GTM: The Generative Topographic Mapping, Neural Computation, vol.39, issue.1, pp.215-234, 1998.
DOI : 10.1007/BF01889678

C. L. Blake and C. J. Merz, UCI repository of machine learning databases, 1998.

G. Celeux, F. Forbes, and N. Payrard, EM procedures using mean field-like approximations for Markov model-based image segmentation, Pattern Recognition, vol.36, issue.1, pp.131-144, 2003.
DOI : 10.1016/S0031-3203(02)00027-4

URL : https://hal.archives-ouvertes.fr/inria-00072526

T. Cover and J. Thomas, Elements of information theory, 1991.

A. P. Dempster, N. M. Laird, and D. B. Rubin, Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society, vol.39, issue.1, pp.1-38, 1977.

Z. Ghahramani and M. I. Jordan, Supervised learning from incomplete data via an EM approach, Advances in Neural Information Processing Systems, pp.120-127

M. Girolami, The topographic organization and visualization of binary data using multivariate-Bernoulli latent variable models, IEEE Transactions on Neural Networks, vol.12, issue.6, pp.1367-1374, 2001.
DOI : 10.1109/72.963773

T. Graepel, M. Burger, and K. Obermayer, Self-organizing maps: Generalizations and new optimization techniques, Neurocomputing, vol.21, issue.1-3, pp.173-190, 1998.
DOI : 10.1016/S0925-2312(98)00035-6

T. Heskes, Self-organizing maps, vector quantization, and mixture modeling, IEEE Transactions on Neural Networks, vol.12, issue.6, pp.1299-1305, 2001.
DOI : 10.1109/72.963766

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.27.6418

M. I. Jordan, Z. Ghahramani, T. Jaakola, and L. K. Saul, An Introduction to Variational Methods for Graphical Models, Machine Learning, vol.37, pp.183-233, 1999.
DOI : 10.1007/978-94-011-5014-9_5

A. Kaban and M. Girolami, A combined latent class and trait model for the analysis and visualization of discrete data, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.23, issue.8, pp.859-872, 2001.
DOI : 10.1109/34.946989

M. J. Kearns, Y. Mansour, and A. Y. Ng, An Information-Theoretic Analysis of Hard and Soft Assignment Methods for Clustering, Learning in Graphical Models, pp.495-520, 1998.
DOI : 10.1007/978-94-011-5014-9_18

T. Kohonen, Self-organizing maps, 2001.

T. Kohonen, S. Kaski, and H. Lappalainen, Self-Organized Formation of Various Invariant-Feature Filters in the Adaptive-Subspace SOM, Neural Computation, vol.58, issue.6, pp.1321-1344, 1997.
DOI : 10.1209/0295-5075/10/7/015

T. Kostiainen and J. Lampinen, On the generative probability density model in the self-organizing map, Neurocomputing, vol.48, issue.1-4, pp.217-228, 2002.
DOI : 10.1016/S0925-2312(01)00649-X

J. Laaksonen, K. Koskela, S. Laakso, and E. Oja, Self-Organising Maps as a Relevance Feedback Technique in Content-Based Image Retrieval, Pattern Analysis & Applications, vol.4, issue.2-3, pp.140-152, 2001.
DOI : 10.1007/PL00014575

G. J. Mclachlan and D. Peel, Finite mixture models, 2000.
DOI : 10.1002/0471721182

R. M. Neal and G. E. Hinton, A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants, Learning in Graphical Models, pp.355-368, 1998.
DOI : 10.1007/978-94-011-5014-9_12

S. Negri and L. Belanche, Heterogeneous Kohonen Networks, Connectionist Models of Neurons, Learning Processes and Artificial Intelligence : 6th Int. Work-Conference on Artificial and Natural Neural Networks, pp.243-252, 2001.
DOI : 10.1007/3-540-45720-8_28

D. De-ridder, J. Kittler, O. Lemmers, and R. P. Duin, The adaptive subspace map for texture segmentation, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, pp.216-220, 2000.
DOI : 10.1109/ICPR.2000.905306

S. T. Roweis, L. K. Saul, and G. E. Hinton, Global coordination of local linear models, Advances in Neural Information Processing Systems 14, 2002.

M. E. Tipping and C. M. Bishop, Mixtures of Probabilistic Principal Component Analyzers, Neural Computation, vol.2, issue.1, pp.443-482, 1999.
DOI : 10.1007/BF00162527

A. Utsugi, Density Estimation by Mixture Models with Smoothing Priors, Neural Computation, vol.39, issue.8, pp.2115-2135, 1998.
DOI : 10.1162/neco.1997.9.3.623

J. J. Verbeek, N. Vlassis, and B. J. Kröse, Coordinating Principal Component Analyzers, Proc. Int. Conf. on Artificial Neural Networks, pp.914-919, 2002.
DOI : 10.1007/3-540-46084-5_148

URL : https://hal.archives-ouvertes.fr/inria-00321498

J. J. Verbeek, N. Vlassis, and B. J. Kröse, Self-organization by optimizing free-energy, Proc. of European Symposium on Artificial Neural Networks. D-side, 2003.
URL : https://hal.archives-ouvertes.fr/inria-00321491

J. J. Verbeek, N. Vlassis, and J. R. Nunnink, A variational EM algorithm for large-scale mixture modeling, Proc. 8th Ann. Conf. of the Advanced School for Computing and Imaging Het Heijderbos, 2003.
URL : https://hal.archives-ouvertes.fr/inria-00321486