B. L. Adam, Y. Qu, J. W. Davis, M. D. Ward, M. A. Clements et al., Serum protein fingerprinting coupled with a pattern-matching algorithm distinguishes prostate cancer from benign prostate hyperplasia and healthy men, Cancer Research, issue.13, pp.623609-3614, 2002.

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

J. Baek, G. J. Mclachlan, and L. Flack, Mixtures of Factor Analyzers with Common Factor Loadings: Applications to the Clustering and Visualisation of High-Dimensional Data, IEEE Transactions on Pattern Analysis and Machine Intelligence, pp.1-13, 2009.

J. Banfield and A. E. Raftery, Model-Based Gaussian and Non-Gaussian Clustering, Biometrics, vol.49, issue.3, pp.803-821, 1993.
DOI : 10.2307/2532201

M. Barker and W. Rayens, Partial least squares for discrimination, Journal of Chemometrics, vol.10, issue.3, pp.166-173, 2003.
DOI : 10.1002/cem.785

R. Bellman, Dynamic Programming, 1957.

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, 2001.
DOI : 10.1109/34.865189

C. Biernacki and J. Jacques, A generative model for rank data based on insertion sort algorithm, Computational Statistics & Data Analysis, vol.58, issue.0, pp.162-176, 2013.
DOI : 10.1016/j.csda.2012.08.008

C. Bouveyron and C. Brunet, Probabilistic Fisher discriminant analysis: A robust and flexible alternative to Fisher discriminant analysis, Neurocomputing, vol.90, issue.1, pp.12-22, 2012.
DOI : 10.1016/j.neucom.2011.11.027

URL : https://hal.archives-ouvertes.fr/hal-00609007

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 and C. Brunet, The FisherEM package for the R software, 2012.

C. Bouveyron and C. Brunet, Theoretical and practical considerations on the convergence properties of the Fisher-EM algorithm, Journal of Multivariate Analysis, vol.109, pp.29-41, 2012.
DOI : 10.1016/j.jmva.2012.02.012

URL : https://hal.archives-ouvertes.fr/hal-00633744

C. Bouveyron and C. Brunet, Discriminative variable selection for clustering with the sparse Fisher-EM algorithm, Computational Statistics, vol.35, issue.5, 2013.
DOI : 10.1007/s00180-013-0433-6

URL : https://hal.archives-ouvertes.fr/hal-00707053

C. Bouveyron, O. Devos, L. Duponchel, S. Girard, J. Jacques et al., Gaussian mixture models for the classification of high-dimensional vibrational spectroscopy data, Journal of Chemometrics, vol.24, pp.11-12719, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00459947

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

C. Bouveyron and J. Jacques, Model-based Clustering of Time Series in Group-specific Functional Subspaces Advances in Data Analysis and Classification, pp.281-300, 2011.

G. Celeux and G. Govaert, Gaussian parsimonious clustering models, Pattern Recognition, vol.28, issue.5, pp.781-793, 1995.
DOI : 10.1016/0031-3203(94)00125-6

URL : https://hal.archives-ouvertes.fr/inria-00074643

G. Celeux, M. Martin-magniette, C. Maugis, and A. E. Raftery, Letter to the editor, Journal of the American Statistical Association, issue.493, p.106, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00944081

W. C. Chang, On Using Principal Components Before Separating a Mixture of Two Multivariate Normal Distributions, Applied Statistics, vol.32, issue.3, pp.267-275, 1983.
DOI : 10.2307/2347949

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

O. Devos, C. Ruckebusch, A. Durand, L. Duponchel, and J. Huvenne, Support vector machines (SVM) in near infrared (NIR) spectroscopy: Focus on parameters optimization and model interpretation, Chemometrics and Intelligent Laboratory Systems, vol.96, issue.1, pp.27-33, 2009.
DOI : 10.1016/j.chemolab.2008.11.005

URL : https://hal.archives-ouvertes.fr/hal-00368855

B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani, Least angle regression. The Annals of Statistics, pp.407-499, 2004.

R. A. Fisher, THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS, Annals of Eugenics, vol.59, issue.2, pp.179-188, 1936.
DOI : 10.1111/j.1469-1809.1936.tb02137.x

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

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

J. H. Friedman, Regularized Discriminant Analysis, Journal of the American Statistical Association, vol.33, issue.405, pp.165-175, 1989.
DOI : 10.1080/01621459.1989.10478752

G. Galimberti, A. Montanari, and C. Viroli, Penalized factor mixture analysis for variable selection in clustered data, Computational Statistics & Data Analysis, vol.53, issue.12, pp.4301-4310, 2009.
DOI : 10.1016/j.csda.2009.05.025

Z. Ghahramani and G. E. Hinton, The EM algorithm for factor analyzers, 1997.

I. Guyon and A. Elisseeff, An introduction to variable and feature selection, Journal of Machine Learning Research, vol.3, pp.1157-1182, 2003.

T. Hastie, A. Buja, and R. Tibshirani, Penalized discriminant analysis. The Annals of Statistics, pp.73-102, 1995.

T. Hastie and W. Stuetzle, Principal Curves, Journal of the American Statistical Association, vol.26, issue.406, pp.502-516, 1989.
DOI : 10.1080/03610927508827223

T. Hastie and R. Tibshirani, Discriminant analysis by gaussian mixture, Journal of the Royal Statistical Society, vol.58, issue.1, pp.155-176, 1996.

T. Hofmann, B. Schölkopf, and A. Smola, Kernel methods in machine learning, The Annals of Statistics, vol.36, issue.3, pp.1171-1220, 2008.
DOI : 10.1214/009053607000000677

H. Hotelling, Analysis of a complex of statistical variables into principal components., Journal of Educational Psychology, vol.24, issue.6, pp.417-441, 1933.
DOI : 10.1037/h0071325

J. Jacques and C. Preda, Funclust: A curves clustering method using functional random variables density approximation, Neurocomputing, vol.112, pp.164-171, 2013.
DOI : 10.1016/j.neucom.2012.11.042

URL : https://hal.archives-ouvertes.fr/hal-00628247

J. Jacques and C. Preda, Model-based clustering for multivariate functional data, Computational Statistics & Data Analysis, vol.71
DOI : 10.1016/j.csda.2012.12.004

URL : https://hal.archives-ouvertes.fr/hal-00943732

T. Kohonen, Self-Organizing Maps, 1995.

M. Law, M. Figueiredo, and A. Jain, Simultaneous feature selection and clustering using mixture models, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26, issue.9, pp.1154-1166, 2004.
DOI : 10.1109/TPAMI.2004.71

G. Lee and C. Scott, EM algorithms for multivariate Gaussian mixture models with truncated and censored data, Computational Statistics & Data Analysis, vol.56, issue.9, pp.2816-2829, 2012.
DOI : 10.1016/j.csda.2012.03.003

B. G. Lindsay, Mixture models: Theory, geometry and applications, NSF-CBMS Regional Conference Series in Probability and Statistics, 1995.

I. Manolopoulou, T. B. Kepler, and D. M. , Mixtures of Gaussian wells: Theory, computation, and application, Computational Statistics & Data Analysis, vol.56, issue.12, pp.3809-3820, 2012.
DOI : 10.1016/j.csda.2012.03.027

C. Maugis, G. Celeux, and M. Martin-magniette, Variable Selection for Clustering with Gaussian Mixture Models, Biometrics, vol.100, issue.3, pp.701-709, 2009.
DOI : 10.1111/j.1541-0420.2008.01160.x

URL : https://hal.archives-ouvertes.fr/inria-00153057

C. Maugis, G. Celeux, and M. Martin-magniette, Variable selection in model-based clustering: A general variable role modeling, Computational Statistics & Data Analysis, vol.53, issue.11, pp.3872-3882, 2009.
DOI : 10.1016/j.csda.2009.04.013

URL : https://hal.archives-ouvertes.fr/inria-00342108

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

G. J. Mclachlan and T. Krishnan, The EM algorithm and extensions, 1997.

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

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

P. D. Mcnicholas, Model-based classification using latent Gaussian mixture models, Journal of Statistical Planning and Inference, vol.140, issue.5, pp.1175-1181, 2010.
DOI : 10.1016/j.jspi.2009.11.006

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

P. D. Mcnicholas and T. B. Murphy, Model-based clustering of microarray expression data via latent Gaussian mixture models, Bioinformatics, vol.26, issue.21, pp.2705-2712, 2010.
DOI : 10.1093/bioinformatics/btq498

V. Melnykov and I. Melnykov, Initializing the EM algorithm in Gaussian mixture models with an unknown number of components, Computational Statistics & Data Analysis, vol.56, issue.6, pp.1381-1395, 2012.
DOI : 10.1016/j.csda.2011.11.002

A. Mkhadri, G. Celeux, and A. Nasrollah, Regularization in discriminant analysis: an overview, Computational Statistics & Data Analysis, vol.23, issue.3, pp.403-423, 1997.
DOI : 10.1016/S0167-9473(96)00043-6

A. Montanari and C. Viroli, Heteroscedastic factor mixture analysis, Statistical Modelling, vol.10, issue.4, pp.441-460, 2010.
DOI : 10.1177/1471082X0901000405

A. O-'hagan, T. B. Murphy, and I. C. Gormley, Computational aspects of fitting mixture models via the expectationmaximization algorithm, Computational Statistics and Data Analysis, vol.56, issue.12, pp.3843-3864, 2012.

W. Pan and X. Shen, Penalized model-based clustering with application to variable selection, Journal of Machine Learning Research, vol.8, pp.1145-1164, 2007.

R. W. Pinheiro, Chemometrics With R: Multivariate Data Analysis in the Natural Sciences and Life Sciences

A. E. 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

D. Rubin and D. Thayer, EM algorithms for ML factor analysis, Psychometrika, vol.34, issue.1, pp.69-76, 1982.
DOI : 10.1007/BF02293851

G. Sanguinetti, Dimensionality reduction of clustered datasets, IEEE Transactions On Pattern Analysis And Machine Intelligence, vol.30, issue.3, pp.1-29, 2008.

B. Schölkopf, A. Smola, and K. Müller, Nonlinear Component Analysis as a Kernel Eigenvalue Problem, Neural Computation, vol.20, issue.5, pp.1299-1319, 1998.
DOI : 10.1007/BF02281970

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

C. Spearman, The Proof and Measurement of Association between Two Things, The American Journal of Psychology, vol.15, issue.1, pp.72-101, 1904.
DOI : 10.2307/1412159

P. M. Steiner and M. Hudec, Classification of large data sets with mixture models via sufficient EM, Computational Statistics & Data Analysis, vol.51, issue.11, pp.5416-5428, 2007.
DOI : 10.1016/j.csda.2006.09.014

M. E. Tipping and C. M. Bishop, Probabilistic Principal Component Analysis, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.61, issue.3, 1997.
DOI : 10.1111/1467-9868.00196

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

S. Wang and J. Zhou, Variable Selection for Model-Based High-Dimensional Clustering and Its Application to Microarray Data, Biometrics, vol.101, issue.2, pp.440-448, 2008.
DOI : 10.1111/j.1541-0420.2007.00922.x

S. Wold, Pattern recognition by means of disjoint principal components models, Pattern Recognition, vol.8, issue.3, pp.127-139, 1976.
DOI : 10.1016/0031-3203(76)90014-5

S. Wold, M. Sjöström, and L. Eriksson, PLS regression: a basic tool of chemometrics. Chemometrics and intelligent laboratory systems, pp.109-130, 2001.

B. Xie, W. Pan, and X. Shen, Penalized model-based clustering with cluster-specific diagonal covariance matrices and grouped variables, Electronic Journal of Statistics, vol.2, issue.0, pp.168-212, 2008.
DOI : 10.1214/08-EJS194

B. Xie, W. Pan, and X. Shen, Penalized mixtures of factor analyzers with application to clustering high-dimensional microarray data, Bioinformatics, vol.26, issue.4, pp.501-508, 2010.
DOI : 10.1093/bioinformatics/btp707

R. Yoshida, T. Higuchi, and S. Imoto, A mixed factors model for dimension reduction and extraction of a group structure in gene expression data, Proceedings. 2004 IEEE Computational Systems Bioinformatics Conference, 2004. CSB 2004., pp.161-172, 2004.
DOI : 10.1109/CSB.2004.1332429

R. Yoshida, T. Higuchi, S. Imoto, and S. Miyano, ArrayCluster: an analytic tool for clustering, data visualization and module finder on gene expression profiles, Bioinformatics, vol.22, issue.12, pp.1538-1539, 2006.
DOI : 10.1093/bioinformatics/btl129

Z. Zhang, G. Dai, and M. I. Jordan, A Flexible and Efficient Algorithm for Regularized Fisher Discriminant Analysis, Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, pp.632-647, 2009.
DOI : 10.1007/978-3-642-04174-7_41