T. Alexandrov, J. Decker, B. Mertens, A. Deelder, R. Tollenaar et al., Biomarker discovery in MALDI-TOF serum protein profiles using discrete wavelet transformation, Bioinformatics, vol.25, issue.5, pp.643-649, 2009.
DOI : 10.1093/bioinformatics/btn662

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, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.32, issue.7, pp.1298-1309, 2009.
DOI : 10.1109/TPAMI.2009.149

R. Bellman, Dynamic Programming, 1957.

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.561-575, 2003.
DOI : 10.1016/S0167-9473(02)00163-9

H. Bock, Probabilistic models in cluster analysis, Computational Statistics & Data Analysis, vol.23, issue.1, pp.5-28, 1996.
DOI : 10.1016/0167-9473(96)88919-5

C. Bouveyron and C. Brunet, Simultaneous model-based clustering and visualization in the Fisher discriminative subspace, Statistics and Computing, vol.20, issue.2, 2011.
DOI : 10.1007/s11222-011-9249-9

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

C. Bouveyron, G. Celeux, and S. Girard, Intrinsic dimension estimation by maximum likelihood in isotropic probabilistic PCA, Pattern Recognition Letters, vol.32, issue.14, pp.1706-1713, 2011.
DOI : 10.1016/j.patrec.2011.07.017

URL : https://hal.archives-ouvertes.fr/hal-00440372

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

R. Cattell, The Scree Test For The Number Of Factors, Multivariate Behavioral Research, vol.1, issue.2, pp.245-276, 1966.
DOI : 10.1207/s15327906mbr0102_10

G. Celeux and J. Diebolt, The SEM algorithm: a probabilistic teacher algorithm from the EM algorithm for the mixture problem, Computational Statistics Quaterly, vol.2, issue.1, pp.73-92, 1985.

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, Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society, vol.39, issue.1, pp.1-38, 1977.

C. Fraley and A. Raftery, MCLUST: Software for Model-Based Cluster Analysis, Journal of Classification, vol.16, issue.2, pp.297-306, 1999.
DOI : 10.1007/s003579900058

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

C. Hennig, Methods for merging Gaussian mixture components Advances in Data Analysis and Classification, pp.3-34, 2010.

G. Mclachlan, Discriminant Analysis and Statistical Pattern Recognition, 1992.
DOI : 10.1002/0471725293

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, Model-based clustering of longitudinal data, Canadian Journal of Statistics, vol.7, issue.1, pp.153-168, 2008.
DOI : 10.1002/cjs.10047

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

T. Pavlenko, On feature selection, curse-of-dimensionality and error probability in discriminant analysis, Journal of Statistical Planning and Inference, vol.115, issue.2, pp.565-584, 2003.
DOI : 10.1016/S0378-3758(02)00166-0

T. Pavlenko and D. Rosen, Effect of dimensionality on discrimination, Statistics, vol.9, issue.3, pp.191-213, 2001.
DOI : 10.1016/0031-3203(90)90100-Y

R. Development and C. Team, R: A Language and Environment for Statistical Computing . R Foundation for Statistical Computing, 2011.

A. Raftery and N. Dean, Variable Selection for Model-Based Clustering, Journal of the American Statistical Association, vol.101, issue.473, pp.168-178, 2006.
DOI : 10.1198/016214506000000113

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

D. Scott and J. Thompson, Probability density estimation in higher dimensions, Fifteenth Symposium in the Interface, pp.173-179, 1983.

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