C. Aggarwal, J. Han, J. Wang, and P. Yu, A Framework for Projected Clustering of High Dimensional Data Streams, Proceedings of the Thirtieth international conference on Very large data bases-Volume, pp.852-863, 2004.
DOI : 10.1016/B978-012088469-8.50075-9

H. Akaike, Likelihood of a model and information criteria, Journal of Econometrics, vol.16, issue.1, pp.3-14, 1981.
DOI : 10.1016/0304-4076(81)90071-3

O. Arandjelovi´carandjelovi´c and R. Cipolla, Incremental learning of temporally-coherent Gaussian mixture models, 2006.

B. Babcock, M. Datar, R. Motwani, and L. O-'callaghan, Maintaining variance and k-medians over data stream windows, Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems , PODS '03, pp.234-243, 2003.
DOI : 10.1145/773153.773176

J. Baek, G. Mclachlan, and L. Flack, Mixtures of factor analyzers with common factor loadings: Applications to the clustering and visualization of high-dimensional data. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.32, issue.7, pp.1298-1309, 2010.

D. Bartholomew, M. Knott, and I. Moustaki, Latent variable models and factor analysis: a unified approach, 2011.
DOI : 10.1002/9781119970583

A. Basilevsky, Statistical factor analysis and related methods: theory and applications, 2009.
DOI : 10.1002/9780470316894

C. Biernacki, G. Celeux, and G. Govaert, Assessing a mixture model for clustering with the integrated completed likelihood. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.22, issue.7, pp.719-725, 2000.

C. Bouveyron and C. Brunet, Simultaneous model-based clustering and visualization in the Fisher discriminative subspace, Statistics and Computing, vol.20, issue.2, pp.301-324, 2012.
DOI : 10.1007/s11222-011-9249-9

URL : https://hal.archives-ouvertes.fr/hal-00492406

C. Bouveyron, S. Girard, and C. Schmid, High-dimensional data clustering, Computational Statistics & Data Analysis, vol.52, issue.1, pp.502-519, 2007.
DOI : 10.1016/j.csda.2007.02.009

URL : https://hal.archives-ouvertes.fr/inria-00548573

C. Bouveyron, S. Girard, and C. Schmid, High-Dimensional Discriminant Analysis, Communications in Statistics - Theory and Methods, vol.1, issue.14, pp.2607-2623, 2007.
DOI : 10.1214/aos/1176344136

URL : https://hal.archives-ouvertes.fr/inria-00548516

O. Cappé and E. Moulines, Online EM algorithm for latent data models, Journal of the Royal Statistics Society: Series B (Statistical Methodology), vol.71, pp.1-21, 2009.

G. Celeux and G. Govaert, A classification EM algorithm for clustering and two stochastic versions, Computational Statistics & Data Analysis, vol.14, issue.3, pp.315-332, 1992.
DOI : 10.1016/0167-9473(92)90042-E

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

A. Dempster, N. Laird, and D. Rubin, Others: Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society. Series BMethodological), vol.39, issue.1, pp.1-38, 1977.

P. Domingos and G. Hulten, A general method for scaling up machine learning algorithms and its application to clustering, MACHINE LEARNING-INTERNATIONAL WORKSHOP THEN CONFERENCE, pp.106-113, 2001.

R. Duda, P. Hart, and D. Stork, Pattern classification and scene analysis, 1995.

M. Figueiredo and A. Jain, Unsupervised learning of finite mixture models. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.24, issue.3, pp.381-396, 2002.

C. Fraley and A. Raftery, Model-Based Clustering, Discriminant Analysis, and Density Estimation, Journal of the American Statistical Association, vol.97, issue.458, pp.611-631, 2002.
DOI : 10.1198/016214502760047131

M. Gaber, A. Zaslavsky, and S. Krishnaswamy, Mining data streams, ACM SIGMOD Record, vol.34, issue.2, pp.18-26, 2005.
DOI : 10.1145/1083784.1083789

Z. Ghahramani and G. Hinton, The em algorithm for mixtures of factor analyzers, 1996.

S. Guha, N. Mishra, R. Motwani, and L. O-'callaghan, Clustering data streams In: Foundations of computer science, 2000. proceedings, IEEE, pp.359-366, 2000.

P. Hall, Y. Hicks, and T. Robinson, A method to add gaussian mixture models, 2005.

P. Hall, D. Marshall, and R. Martin, Incremental Eigenanalysis for Classification, Procedings of the British Machine Vision Conference 1998, pp.286-295, 1998.
DOI : 10.5244/C.12.29

J. Jacques, C. Bouveyron, S. Girard, O. Devos, L. Duponchel et al., Gaussian mixture models for the classification of high-dimensional vibrational spectroscopy data, Journal of Chemometrics, vol.86, issue.2, pp.11-12, 2010.
DOI : 10.1002/cem.1355

URL : https://hal.archives-ouvertes.fr/hal-00459947

B. Lindsay, Mixture models: theory, geometry and applications. In: NSF-CBMS regional conference series in probability and statistics, JSTOR, 1995.

J. Macqueen, Some methods for classification and analysis of multivariate observations, Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, p.14, 1967.

G. Mclachlan and T. Krishnan, The em algorithm and extensions, 1997.

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

G. Mclachlan, D. Peel, and R. Bean, Modelling high-dimensional data by mixtures of factor analyzers, Computational Statistics & Data Analysis, vol.41, issue.3-4, pp.379-388, 2003.
DOI : 10.1016/S0167-9473(02)00183-4

P. Mcnicholas and B. Murphy, Parsimonious Gaussian mixture models, Statistics and Computing, vol.61, issue.3, pp.285-296, 2008.
DOI : 10.1007/s11222-008-9056-0

R. Neal and G. Hinton, A view of the EM algorithm that justifies incremental, sparse, and other variants. Learning in graphical models 89, pp.355-368, 1998.

O. 'callaghan, L. Mishra, N. Meyerson, A. Guha, S. Motwani et al., Streaming-data algorithms for high-quality clustering, Proceedings. 18th International Conference on, pp.685-694, 2002.

A. Samé, C. Ambroise, and G. Govaert, An online classification EM algorithm based on the mixture model, Statistics and Computing, vol.46, issue.3, pp.209-218, 2007.
DOI : 10.1007/s11222-007-9017-z

G. Schwarz, Estimating the Dimension of a Model, The Annals of Statistics, vol.6, issue.2, pp.461-464, 1978.
DOI : 10.1214/aos/1176344136

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

M. Tipping and C. Bishop, Probabilistic Principal Component Analysis, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.61, issue.3, pp.611-622, 1999.
DOI : 10.1111/1467-9868.00196

D. Titterington, Recursive parameter estimation using incomplete data, Journal of the Royal Statistical Society. Series B (Methodological), vol.46, issue.2, pp.257-267, 1984.

N. Ueda, R. Nakano, Z. Ghahramani, and G. Hinton, SMEM Algorithm for Mixture Models, Neural Computation, vol.21, issue.9, pp.2109-2128, 2000.
DOI : 10.1207/s15327906mbr0503_6

C. Wu, On the Convergence Properties of the EM Algorithm, The Annals of Statistics, vol.11, issue.1, pp.95-103, 1983.
DOI : 10.1214/aos/1176346060