Identifiability of parameters in latent structure models with many observed variables, The Annals of Statistics, vol.37, issue.6A, pp.3099-3132, 2009. ,
DOI : 10.1214/09-AOS689
URL : https://hal.archives-ouvertes.fr/hal-00591202
A generalized maximum entropy approach to bregman co-clustering and matrix approximation, Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '04, pp.1919-1986, 2007. ,
DOI : 10.1145/1014052.1014111
Sélection de modèle pour la classification non supervisée. Choix du nombre de classes, 2009. ,
Combining Mixture Components for Clustering, Journal of Computational and Graphical Statistics, vol.19, issue.2, pp.332-353, 2010. ,
DOI : 10.1198/jcgs.2010.08111
URL : https://hal.archives-ouvertes.fr/inria-00321090
Assessing a mixture model for clustering with the integrated completed likelihood, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, issue.7, pp.719-725, 2000. ,
DOI : 10.1109/34.865189
Exact and Monte Carlo calculations of integrated likelihoods for the latent class model, Journal of Statistical Planning and Inference, vol.140, issue.11, pp.2991-3002, 2010. ,
DOI : 10.1016/j.jspi.2010.03.042
URL : https://hal.archives-ouvertes.fr/inria-00310137
Practical Identifiability of Finite Mixtures of Multivariate Bernoulli Distributions, Neural Computation, vol.36, issue.1, pp.141-152, 2000. ,
DOI : 10.1207/s15327906mbr0503_6
Stochastic versions of the em algorithm, Computational Statistics Quaterly, vol.2, pp.73-82, 1985. ,
URL : https://hal.archives-ouvertes.fr/inria-00074164
Consistency of maximum-likelihood and variational estimators in the stochastic block model, Electronic Journal of Statistics, vol.6, issue.0, pp.1847-1899, 2012. ,
DOI : 10.1214/12-EJS729
URL : https://hal.archives-ouvertes.fr/hal-00593644
A mixture model for random graphs, Statistics and Computing, vol.4, issue.2, pp.173-183, 2008. ,
DOI : 10.1007/s11222-007-9046-7
URL : https://hal.archives-ouvertes.fr/inria-00070186
Maximum likelihood from incomplete data via the EM algorithm (with discussion), Journal of the Royal Statistical Society, Series B, vol.39, pp.1-38, 1977. ,
Finite mixture and Markov switching models. Springer series in statistics, 2006. ,
Mixtures : estimation and applications, chapter Dealing with label switching under model uncertainty, pp.193-218, 2011. ,
Algorithme de classification d'un tableau de contingence, First international symposium on data analysis and informatics, pp.487-500, 1977. ,
Classification croisée, 1983. ,
Block clustering with Bernoulli mixture models: Comparison of different approaches, Computational Statistics & Data Analysis, vol.52, issue.6, pp.3233-3245, 2008. ,
DOI : 10.1016/j.csda.2007.09.007
Latent block model for contingency table. Communication in Statistics -Theory and Methods, pp.416-425, 2010. ,
URL : https://hal.archives-ouvertes.fr/hal-00447792
Non-uniqueness in probabilistic numerical identification of bacteria, Journal of Applied Probability, vol.132, issue.02, pp.542-548, 1994. ,
DOI : 10.1099/00207713-24-4-494
Analyzing in situ gene expression in the mouse brain with image registration, feature extraction and block clustering, BMC Bioinformatics, vol.8, issue.Suppl 10, p.5, 2007. ,
DOI : 10.1186/1471-2105-8-S10-S5
Consistent estimation of the order of mixture models, Sankhya Series A, vol.62, pp.49-66, 2000. ,
Méthodes bayésiennes variationnelles: concepts et applications en neuroimagerie, pp.107-131, 2010. ,
Model selection for the binary latent block model, Proceedings of COMPSTAT 2012, 2012. ,
URL : https://hal.archives-ouvertes.fr/hal-00924210
Estimation and selection for the latent block model on categorical data, Statistics and Computing, vol.22, issue.2, 2013. ,
DOI : 10.1007/s11222-014-9472-2
URL : https://hal.archives-ouvertes.fr/hal-00802764
Sélection de modèle pour la classification croisée de données continues, 2012. ,
Un protocole de simulation de données pour la classification croisée, 44ème journées de statistique, 2012. ,
Biclustering algorithms for biological data analysis: a survey, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.1, issue.1, pp.24-45, 2004. ,
DOI : 10.1109/TCBB.2004.2
Convergence of the groups posterior distribution in latent or stochastic block models. arXiv preprint, 2013. ,
URL : https://hal.archives-ouvertes.fr/hal-01174515
The EM algorithm and extensions, 2008. ,
Finite Mixture Models, 2000. ,
DOI : 10.1002/0471721182
Nonparametric bayesian biclustering, 2007. ,
Bayesian finite mixtures with an unknown number of components: The allocation sampler, Statistics and Computing, vol.14, issue.2, pp.147-162, 2007. ,
DOI : 10.1007/s11222-006-9014-7
Asymptotic behaviour of the posterior distribution in overfitted mixture models, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.6, issue.5, pp.689-710, 2011. ,
DOI : 10.1111/j.1467-9868.2011.00781.x
URL : https://hal.archives-ouvertes.fr/hal-00641475
Estimating the Dimension of a Model, The Annals of Statistics, vol.6, issue.2, pp.461-464, 1978. ,
DOI : 10.1214/aos/1176344136
Bayesian Co-clustering, 2008 Eighth IEEE International Conference on Data Mining, pp.530-539, 2008. ,
DOI : 10.1109/ICDM.2008.91
Block clustering with collapsed latent block models, Statistics and Computing, vol.28, issue.2, pp.415-428, 2012. ,
DOI : 10.1007/s11222-011-9233-4
URL : http://arxiv.org/abs/1011.2948