H. Akaike, A new look at the statistical model identification, IEEE Transactions on Automatic Control, vol.19, issue.6, pp.716-723, 1974.
DOI : 10.1109/TAC.1974.1100705

M. Aitkin and &. D. Rubin, Estimation and hypothesis testing in finite mixture models, Journal of the Royal Statistical Society. Series B, vol.47, issue.1, pp.67-75, 1985.

J. Andrews and &. P. Mcnicholas, Model-based clustering, classification, and discriminant analysis via mixtures of multivariate t-distributions, Statistics and Computing, vol.1, issue.4, pp.1021-1029, 2012.
DOI : 10.1007/s11222-011-9272-x

H. Bensmail and &. G. Celeux, Regularized Gaussian Discriminant Analysis through Eigenvalue Decomposition, Journal of the American Statistical Association, vol.91, issue.436, pp.1743-1748, 1996.
DOI : 10.1002/0471725293

N. Bouguila, D. Ziou, and &. J. Vaillancourt, Novel Mixtures Based on the Dirichlet Distribution: Application to Data and Image Classification, Machine Learning and Data Mining in Pattern Recognition, pp.172-181, 2003.
DOI : 10.1007/3-540-45065-3_15

C. Bouveyron, M. Fauvel, and &. S. Girard, Kernel discriminant analysis and clustering with parsimonious Gaussian process models, Statistics and Computing, vol.22, issue.5, pp.1143-1162, 2015.
DOI : 10.1007/s11222-014-9505-x
URL : https://hal.archives-ouvertes.fr/hal-00707056

L. Bergé, C. Bouveyron, and &. S. Girard, Package for Model-Based Clustering and Discriminant Analysis of High-Dimensional Data, Journal of Statistical Software, vol.46, issue.6, pp.1-29, 2012.
DOI : 10.18637/jss.v046.i06

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

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

G. Celeux and &. G. Govaert, Clustering criteria for discrete data and latent class models, Journal of Classification, vol.4, issue.4, pp.157-176, 1991.
DOI : 10.1007/BF02616237
URL : https://hal.archives-ouvertes.fr/inria-00075437

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. Series B, vol.39, pp.1-38, 1977.

F. Forbes and &. D. Wraith, A new family of multivariate heavy-tailed distributions with variable marginal amounts of tailweight: application to robust clustering, Statistics and Computing, vol.94, issue.1, pp.971-984, 2014.
DOI : 10.1007/s11222-013-9414-4

D. Fraix-burnet and &. D. Valls-gabaud, Regression methods for astrophysics, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01115079

C. Fraley and &. E. Raftery, Enhanced Model-Based Clustering, Density Estimation, and Discriminant Analysis Software: MCLUST, Journal of Classification, vol.20, issue.2, pp.263-286, 2003.
DOI : 10.1007/s00357-003-0015-3

B. C. Franczak, R. P. Browne, and &. D. Mcnicholas, Mixtures of Shifted AsymmetricLaplace Distributions, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.36, issue.6, pp.1149-1157, 2014.
DOI : 10.1109/TPAMI.2013.216

M. Goldstein and &. W. Dillon, Discrete discriminant analysis, 1978.

J. Hartigan and &. M. Wong, Algorithm AS 136: A K-Means Clustering Algorithm, Applied Statistics, vol.28, issue.1, pp.100-108, 1979.
DOI : 10.2307/2346830

I. Jolliffe, Principal component analysis, 2002.
DOI : 10.1007/978-1-4757-1904-8

R. Kass and &. A. Raftery, Bayes Factors, Journal of the American Statistical Association, vol.2, issue.430, pp.773-795, 1995.
DOI : 10.1080/01621459.1995.10476572

S. Lee and &. G. Mclachlan, Finite mixtures of multivariate skew t-distributions: some recent and new results, Statistics and Computing, vol.82, issue.4, pp.181-202, 2013.
DOI : 10.1007/s11222-012-9362-4

L. Van-der-maaten, E. Postma, and &. H. Van-den-herik, Dimensionality reduction: A comparative review, Journal of Machine Learning Research, vol.10, pp.66-71, 2009.

G. J. Mclachlan and &. D. Peel, Finite Mixture Models, 2000.
DOI : 10.1002/0471721182

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

F. Vilca, N. Balakrishnan, and &. C. Zeller, Multivariate Skew-Normal Generalized Hyperbolic distribution and its properties, Journal of Multivariate Analysis, vol.128, pp.73-85, 2014.
DOI : 10.1016/j.jmva.2014.03.002

D. Wraith and &. F. Forbes, Location and scale mixtures of Gaussians with flexible tail behaviour: Properties, inference and application to multivariate clustering, Computational Statistics & Data Analysis, vol.90, pp.61-73, 2015.
DOI : 10.1016/j.csda.2015.04.008
URL : https://hal.archives-ouvertes.fr/hal-01254178