A. Anandkumar, D. P. Foster, D. J. Hsu, S. M. Kakade, and Y. Liu, A Spectral Algorithm for Latent Dirichlet Allocation, Advances in Neural Information Processing Systems, pp.917-925, 2012.
DOI : 10.1016/j.jcss.2003.11.008

A. Banerjee, I. Dhillon, J. Ghosh, S. Merugu, and D. S. Modha, 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

C. Biernacki, G. Celeux, and G. Govaert, 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

C. Biernacki, G. Celeux, and G. Govaert, Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models, Computational Statistics & Data Analysis, vol.41, issue.3-4, pp.41561-575, 2003.
DOI : 10.1016/S0167-9473(02)00163-9

D. Blei and J. Lafferty, Correlated topic models Advances in neural information processing systems, p.147, 2006.

D. M. Blei, A. Y. Ng, J. , and M. I. , Latent dirichlet allocation, the Journal of machine Learning research, vol.3, pp.993-1022, 2003.

C. Bouveyron, P. Latouche, and R. Zreik, The stochastic topic block model for the clustering of vertices in networks with textual edges, Statistics and Computing, vol.31, issue.9, 2016.
DOI : 10.1145/1135777.1135807

URL : https://hal.archives-ouvertes.fr/hal-01299161

V. Brault and A. Channarond, Fast and consistent algorithm for the latent block model. arXiv preprint, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01455682

G. Celeux and G. Govaert, A classification EM algorithm for clustering and two stochastic versions, Computational Statistics & Data Analysis, vol.14, issue.3, 1991.
DOI : 10.1016/0167-9473(92)90042-E

URL : https://hal.archives-ouvertes.fr/inria-00075196

E. Côme, A. Randriamanamihaga, L. Oukhellou, and P. Aknin, Spatio-temporal analysis of dynamic origin-destination data using latent dirichlet allocation. application to the vélib? bike sharing system of paris, Proceedings of 93rd Annual Meeting of the Transportation Research Board, 2014.

S. Deerwester, S. Dumais, G. Furnas, T. Landauer, and R. Harshman, Indexing by latent semantic analysis, Journal of the American Society for Information Science, vol.41, issue.6, p.41391, 1990.
DOI : 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9

A. P. Dempster, N. M. Laird, R. , and D. B. , Maximum likelihood from incomplete data via the em algorithm, Journal of the royal statistical society. Series B (methodological), pp.1-38, 1977.

T. George and S. Merugu, A Scalable Collaborative Filtering Framework Based on Co-Clustering, Fifth IEEE International Conference on Data Mining (ICDM'05), p.4, 2005.
DOI : 10.1109/ICDM.2005.14

G. Govaert and M. Nadif, Clustering with block mixture models, Pattern Recognition, vol.36, issue.2, pp.463-473, 2003.
DOI : 10.1016/S0031-3203(02)00074-2

G. Govaert and M. Nadif, 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

G. Govaert and M. Nadif, Latent Block Model for Contingency Table, Communications in Statistics - Theory and Methods, vol.24, issue.3, pp.416-425, 2010.
DOI : 10.1007/978-94-011-5014-9_12

URL : https://hal.archives-ouvertes.fr/hal-00447792

R. J. Hathaway, Another interpretation of the EM algorithm for mixture distributions, Statistics & Probability Letters, vol.4, issue.2, pp.53-56, 1986.
DOI : 10.1016/0167-7152(86)90016-7

T. Hofmann, Probabilistic latent semantic indexing, Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, pp.50-57, 1999.

J. Jacques and C. Biernacki, Model-based co-clustering for ordinal data, Computational Statistics & Data Analysis, vol.123, 2017.
DOI : 10.1016/j.csda.2018.01.014

URL : https://hal.archives-ouvertes.fr/hal-01448299

C. Keribin, V. Brault, G. Celeux, and G. Govaert, Estimation and selection for the latent block model on categorical data, Statistics and Computing, vol.22, issue.2, pp.1201-1216, 2015.
DOI : 10.1007/s11222-011-9233-4

URL : https://hal.archives-ouvertes.fr/hal-01256840

C. Keribin, V. Brault, G. Celeux, and G. Govaert, Model selection for the binary latent block model, Proceedings of COMPSTAT, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00924210

S. Kumar, X. Gao, W. , and I. , Co-clustering for Dual Topic Models, Australasian Joint Conference on Artificial Intelligence, pp.390-402, 2016.
DOI : 10.1137/1.9781611972818.29

S. Lazebnik, C. Schmid, and J. Ponce, Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Volume 2 (CVPR'06), pp.2169-2178, 2006.
DOI : 10.1109/CVPR.2006.68

URL : https://hal.archives-ouvertes.fr/inria-00548585

A. Lomet, Sélection de modèle pour la classification croisée de données continues, 2012.

K. Nigam, A. Mccallum, S. Thrun, M. , and T. , Text classification from labeled and unlabeled documents using em, Machine Learning, vol.39, issue.2/3, pp.103-134, 2000.
DOI : 10.1023/A:1007692713085

C. Papadimitriou, P. Raghavan, H. Tamaki, and S. Vempala, Latent semantic indexing, Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems , PODS '98, pp.159-168, 1998.
DOI : 10.1145/275487.275505

X. Phan, L. Nguyen, and S. Horiguchi, Learning to classify short and sparse text & web with hidden topics from large-scale data collections, Proceeding of the 17th international conference on World Wide Web , WWW '08, pp.91-100, 2008.
DOI : 10.1145/1367497.1367510

A. Podosinnikova, F. Bach, and S. Lacoste-julien, Rethinking lda: moment matching for discrete ica, Advances in Neural Information Processing Systems, pp.514-522, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01225271

W. M. Rand, Objective Criteria for the Evaluation of Clustering Methods, Journal of the American Statistical Association, vol.15, issue.336, pp.846-850, 1971.
DOI : 10.1080/01621459.1963.10500845

M. M. Shafiei and E. E. Milios, Latent Dirichlet Co-Clustering, Sixth International Conference on Data Mining (ICDM'06), pp.542-551, 2006.
DOI : 10.1109/ICDM.2006.94

Y. Teh, D. Newman, and M. Welling, A collapsed variational bayesian inference algorithm for latent Dirichlet allocation Advances in neural information processing systems, pp.1353-1360, 2006.
DOI : 10.21236/ada629956

URL : http://www.dtic.mil/dtic/tr/fulltext/u2/a629956.pdf

K. Than and T. B. Ho, Fully Sparse Topic Models, Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp.490-505, 2012.
DOI : 10.1007/978-3-642-33460-3_37

URL : http://www.jaist.ac.jp/~bao/papers/ECML2012.pdf

U. Von-luxburg, A tutorial on spectral clustering, Statistics and Computing, vol.21, issue.1, pp.395-416, 2007.
DOI : 10.1017/CBO9780511810633

P. Wang, C. Domeniconi, and K. B. Laskey, Latent Dirichlet Bayesian Co-Clustering, Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp.522-537, 2009.
DOI : 10.1073/pnas.0307752101

URL : https://link.springer.com/content/pdf/10.1007%2F978-3-642-04174-7_34.pdf

S. Wang and A. Huang, Penalized nonnegative matrix tri-factorization for co-clustering, Expert Systems with Applications, vol.78, pp.64-73, 2017.
DOI : 10.1016/j.eswa.2017.01.019

J. Wyse and N. Friel, Block clustering with collapsed latent block models, Statistics and Computing, vol.28, issue.2, pp.415-428, 2012.
DOI : 10.1214/aos/1016120364

URL : http://arxiv.org/pdf/1011.2948

J. Wyse, N. Friel, and P. Latouche, Abstract, Network Science, vol.11, issue.01, pp.45-69, 2017.
DOI : 10.1016/j.csda.2007.09.007