A new look at the statistical model identification. Automatic Control, IEEE Transactions on, vol.19, issue.6, pp.716-723, 1974. ,
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
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
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
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
Model-based clustering of high-dimensional data: A review, Computational Statistics & Data Analysis, vol.71, pp.52-78, 2013. ,
DOI : 10.1016/j.csda.2012.12.008
URL : https://hal.archives-ouvertes.fr/hal-00750909
Robust supervised classification with mixture models: Learning from data with uncertain labels, Pattern Recognition, vol.42, issue.11, pp.2649-2658, 2009. ,
DOI : 10.1016/j.patcog.2009.03.027
URL : https://hal.archives-ouvertes.fr/hal-00325263
Model-based clustering of time series in group-specific functional subspaces Advances in Data Analysis and Classification, pp.281-300, 2011. ,
High-dimensional discriminant analysis Communication in Statistics: Theory and Methods, pp.2607-2623, 2007. ,
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
SVM and kernel methods matlab toolbox, Perception Systemes et Information, 2005. ,
Universal multi-task kernels, Journal of Machine Learning Research, vol.68, pp.1615-1646, 2008. ,
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
Semi-Supervised Learning, 2006. ,
DOI : 10.7551/mitpress/9780262033589.001.0001
Kernel K-Means for Categorical Data, Advances in Intelligent Data Analysis VI, pp.739-739, 2005. ,
DOI : 10.1007/11552253_5
The context-tree kernel for strings, Neural Networks, vol.18, issue.8, pp.1111-1123, 2005. ,
DOI : 10.1016/j.neunet.2005.07.010
URL : https://hal.archives-ouvertes.fr/hal-00433583
Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society, Series B, vol.39, issue.1, pp.1-38, 1977. ,
Toward an Optimal Supervised Classifier for the Analysis of Hyperspectral Data, IEEE Transactions on Geoscience and Remote Sensing, vol.42, issue.1, pp.271-277, 2004. ,
DOI : 10.1109/TGRS.2003.817813
Learning multiple tasks with kernel methods, Journal of Machine Learning Research, vol.6, pp.615-637, 2005. ,
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
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, 2014. ,
DOI : 10.1007/s11222-013-9414-4
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
Mercer kernel-based clustering in feature space, IEEE Transactions on Neural Networks, vol.13, issue.3, pp.780-784, 2002. ,
DOI : 10.1109/TNN.2002.1000150
Multiple kernel learning algorithms, Journal of Machine Learning Research, vol.12, pp.2211-2268, 2011. ,
Kernel methods in machine learning. The Annals of Statistics, pp.1171-1220, 2008. ,
Multiple Operator-valued Kernel Learning, Neural Information Processing Systems (NIPS), pp.1172-1080, 2012. ,
URL : https://hal.archives-ouvertes.fr/hal-00677012
Assessing approximate inference for binary Gaussian process classification, Journal of Machine Learning Research, vol.6, pp.1679-1704, 2005. ,
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
Deflation Techniques for an Implicitly Restarted Arnoldi Iteration, SIAM Journal on Matrix Analysis and Applications, vol.17, issue.4, pp.789-821, 1996. ,
DOI : 10.1137/S0895479895281484
Robust mixture modeling using multivariate skew t??distributions, Statistics and Computing, vol.14, issue.3, pp.343-356, 2010. ,
DOI : 10.1007/s11222-009-9128-9
Robust mixture modeling using the skew t distribution, Statistics and Computing, vol.14, issue.2, pp.81-92, 2007. ,
DOI : 10.1007/s11222-006-9005-8
Graph kernels based on tree patterns for molecules, Machine Learning, vol.21, issue.Suppl.??1, pp.3-35, 2009. ,
DOI : 10.1007/s10994-008-5086-2
Discriminant Analysis and Statistical Pattern Recognition, 1992. ,
DOI : 10.1002/0471725293
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
Parsimonious Gaussian mixture models, Statistics and Computing, vol.61, issue.3, pp.285-296, 2008. ,
DOI : 10.1007/s11222-008-9056-0
Fisher discriminant analysis with kernels, Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468), pp.41-48, 1999. ,
DOI : 10.1109/NNSP.1999.788121
Expectation propagation for approximate bayesian inference, Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence, pp.362-369, 2001. ,
Heteroscedastic factor mixture analysis. Statistical Modeling: An International journal, pp.441-460, 2010. ,
Variable selection and updating in model-based discriminant analysis for high dimensional data with food authenticity applications, The Annals of Applied Statistics, vol.4, issue.1, pp.219-223, 2010. ,
DOI : 10.1214/09-AOAS279SUPP
Kernel-based mixture models for classification, Computational Statistics, vol.42, issue.7, 2014. ,
DOI : 10.1007/s00180-014-0535-9
Kernel Discriminant Analysis for Positive Definite and Indefinite Kernels, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.31, issue.6, pp.1017-1032, 2009. ,
DOI : 10.1109/TPAMI.2008.290
Functional Data Analysis. Springer Series in Statistics, Biometrical Journal, vol.40, issue.1, 2005. ,
DOI : 10.1002/(SICI)1521-4036(199804)40:1<56::AID-BIMJ56>3.0.CO;2-#
Gaussian processes for machine learning matlab toolbox, 2006. ,
Gaussian Processes in Machine Learning, 2006. ,
DOI : 10.1162/089976602317250933
Learning with Kernels: Support Vector Machines, Regularization, Optimization , and Beyond, 2001. ,
Nonlinear Component Analysis as a Kernel Eigenvalue Problem, Neural Computation, vol.20, issue.5, pp.1299-1319, 1998. ,
DOI : 10.1007/BF02281970
Estimating the Dimension of a Model, The Annals of Statistics, vol.6, issue.2, pp.461-464, 1978. ,
DOI : 10.1214/aos/1176344136
Kernel Methods for Pattern Analysis, 2004. ,
DOI : 10.1017/CBO9780511809682
Empirical Processes with Applications to Statistics, 1986. ,
DOI : 10.1137/1.9780898719017
Kernels and Regularization on Graphs, Proc. Conf. on Learning Theory and Kernel Machines, pp.144-158, 2003. ,
DOI : 10.1007/978-3-540-45167-9_12
Kernel Trick Embedded Gaussian Mixture Model, Proceedings of the 14th international conference on algorithmic learning theory, pp.159-174, 2003. ,
DOI : 10.1007/978-3-540-39624-6_14
A novel kernel-based maximum a posteriori classification method, Neural Networks, vol.22, issue.7, pp.977-987, 2009. ,
DOI : 10.1016/j.neunet.2008.11.005