Statistical Learning of Latent Data Models for Complex Data Analysis
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Universite´ de Toulon Habilitation `aDiriger les Recherches (HDR) Discipline : Informatique et Math´ematiquesappliqu´ees pr´esent´ee par Faicel CHAMROUKHI Ma^ıtre de Conf´erences, UMR CNRS LSIS Statistical learning of latent data models for complex data analysis Soutenue publiquement le 07 d´ecembre 2015 JURY Geoffrey McLachlan Professor, University of Queensland Rapporteur Australian Academy of Science Fellow Christophe Ambroise Professeur, Universit´ed'Evry Rapporteur Youn`esBennani Professeur, Universit´eParis Nord Rapporteur St´ephaneDerrode Professeur, Ecole Centrale de Lyon Rapporteur Mohamed Nadif Professeur, Universit´eParis 5 Examinateur Christophe Biernacki Professeur, Universit´eLille 1, INRIA Examinateur Herv´eGlotin Professeur, Universit´ede Toulon Examinateur Acknowledgements First, I would like to address all my thanks and express my gratitude to Professor Ge- off McLachlan, Professor Christophe Ambroise, Professor Youn`esBennani and Professor St´ephaneDerrode for having given me the honor of reviewing my habilitation and for the quality of their reports. A very special mention to Geoff; I was also very honored by your visit this year and greatly appreciated all the time you have spent in Toulon. You are inspiring me a lot to continue in this way in science. I would also like to address my very special thanks to Professor Christophe Biernacki, Pro- fessor Mohamed Nadif and Professor Herv´eGlotin for accepting to be part of my habilitation committee. A special warm mention to Professor Herv´eGlotin for these past four years of collaboration in Toulon. I would also like to address my special warm thanks to my colleagues of the DYNI team and the computer science department with whom it is a great pleasure to work. I also extend my warm thanks to all my colleagues of the LSIS lab and the faculty of science, with whom I have worked during these past four years, since I have been recruited in Toulon. This year I am in CNRS research leave and I would like to warmly thank my new colleagues at the lab of mathematics of Lille 1 and at INRIA-Modal for their warm welcome. A particular mention to Professor Christophe Biernacki for having given me the honor to join his team and for his support. I am also grateful to all the colleagues with whom I have collaborated during my research since the beginning of my PhD and I would like to address my thanks and express my gratitude to all of them at the Heudiasyc lab of UTC Compi`egne,the Grettia group of IFSTTAR-Marne, the LiSSi lab of Paris 12, the LIPN lab of Paris 13, the LIPADE lab of Paris 5. A particular mention to my PhD advisors Doctor Allou Sam´e,Doctor Patrice Aknin, and Professor G´erard Govaert. I have been a teaching assistant at Universit´eParis 13 during my first four academic years and I take this opportunity to warmly thank all my colleagues at the computer science department with whom I have collaborated. I am also grateful to my former PhD and MSc students Dr. Marius Bartcus, Dr. Dorra Trabelsi, Dr. Rakia Jaziri, MSc. Ahmed Hosni, MSc. C´elineRabouy, MSc. Ahmad Tay and MSc. Hiba Badri. Thanks to all of you for your collaboration and I wish you all the success in your career and in your life. Finally, I address my thanks to my friends and my family. This manuscript has been finished in august, at Hy`eres. Faicel Chamroukhi Lille, November 26, 2015 Contents 1 Introduction 1 1.1 Contributions during my thesis (2007-2010) . .2 1.2 Contributions after my thesis (2011-2015) . .2 1.2.1 Latent data models for non-stationary multivariate temporal data . .2 1.2.2 Functional data analysis . .2 1.2.3 Bayesian regularization of mixtures for functional data analysis . .3 1.2.4 Bayesian non-parametric parsimonious mixtures for multivariate data . .3 1.2.5 Non-normal mixtures of experts . .4 1.2.6 Applications . .4 2 Latent data models for temporal data segmentation 5 2.1 Introduction . .7 2.1.1 Personal contribution . .8 2.1.2 Problem statement . .9 2.2 Regression with hidden logistic process . .9 2.2.1 The model . .9 2.2.2 Maximum likelihood estimation via a dedicated EM . 10 2.2.3 Experiments . 12 2.2.4 Conclusion . 13 2.2.5 Multiple hidden process regression for joint segmentation of multivariate time series 13 2.3 Multiple hidden logistic process regression . 13 2.3.1 The model . 14 2.3.2 Maximum likelihood estimation via a dedicated EM . 14 2.3.3 Application on human activity time series . 15 2.3.4 Conclusion . 15 2.4 Multiple hidden Markov model regression . 16 2.4.1 The model . 17 2.4.2 Maximum likelihood estimation via a dedicated EM . 17 2.4.3 Application on human activity time series . 17 2.4.4 Conclusion . 18 3 Latent data models for functional data analysis 19 3.1 Introduction . 21 3.1.1 Personal contribution . 22 3.1.2 Mixture modeling framework for functional data . 22 3.2 Mixture of piecewise regressions . 23 3.2.1 The model . 23 3.2.2 Maximum likelihood estimation via a dedicated EM . 24 3.2.3 Maximum classification likelihood estimation via a dedicated CEM . 25 3.2.4 Experiments . 26 3.2.5 Conclusion . 28 3.3 Mixture of hidden Markov model regressions . 30 3.3.1 The model . 30 3.3.2 Maximum likelihood estimation via a dedicated EM . 31 iii 3.3.3 Experiments . 32 3.3.4 Conclusion . 33 3.4 Mixture of hidden logistic process regressions . 34 3.4.1 The model . 34 3.4.2 Maximum likelihood estimation via a dedicated EM algorithm . 35 3.4.3 Experiments . 36 3.4.4 Conclusion . 37 3.5 Functional discriminant analysis . 37 3.5.1 Functional linear discriminant analysis . 38 3.5.2 Functional mixture discriminant analysis . 38 3.5.3 Experiments . 39 3.5.4 Conclusion . 40 4 Bayesian regularization of mixtures for functional data 43 4.1 Introduction . 45 4.1.1 Personal contribution . 46 4.1.2 Regression mixtures . 46 4.2 Regularized regression mixtures for functional data . 47 4.2.1 Introduction . 47 4.2.2 Regularized maximum likelihood estimation via a robust EM-like algorithm . 49 4.2.3 Experiments . 51 4.2.4 Conclusion . 53 4.3 Bayesian mixtures of spatial spline regressions . 54 4.3.1 Bayesian inference by Markov Chain Monte Carlo (MCMC) sampling . 54 4.3.2 Mixtures of spatial spline regressions with mixed-effects . 55 4.3.3 Bayesian spatial spline regression with mixed-effects . 57 4.3.4 Bayesian mixture of spatial spline regressions with mixed-effects . 59 4.3.5 Experiments . 61 4.3.6 Conclusion . 62 5 Bayesian non-parametric parsimonious mixtures for multivariate data 63 5.1 Introduction . 65 5.1.1 Personal contribution . 67 5.2 Finite mixture model model-based clustering . 67 5.2.1 Bayesian model-based clustering . 68 5.2.2 Parsimonious Gaussian mixture models . 68 5.3 Dirichlet Process Parsimonious Mixtures . 69 5.3.1 Dirichlet Process Parsimonious Mixtures . 69 5.3.2 Chinese Restaurant Process parsimonious mixtures . 71 5.3.3 Bayesian inference via Gibbs sampling . 72 5.3.4 Bayesian model comparison via Bayes factors . 73 5.3.5 Experiments . 74 5.4 Conclusion . 77 6 Non-normal mixtures of experts 79 6.1 Introduction . 81 6.1.1 Personal contribution . 82 6.1.2 Mixture of experts for continuous data . 83 6.1.3 The normal mixture of experts model and its MLE . 83 6.2 The skew-normal mixture of experts model . 84 6.2.1 The model . 84 6.2.2 Maximum likelihood estimation via the ECM algorithm . 85 6.3 The t mixture of experts model . 88 6.3.1 The model . ..