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1 INRETS-LTN - Laboratoire des Technologies Nouvelles 2 CERTES EA3481 - Centre d-Etudes et Recherches en Thermique, Environnement et Systèmes Créteil 3 SAMM - Statistique, Analyse et Modélisation Multidisciplinaire SAmos-Marin Mersenne 4 IFSTTAR-GRETTIA - Génie des Réseaux de Transport Terrestres et Informatique Avancée 5 Heudiasyc - Heuristique et Diagnostic des Systèmes Complexes Compiègne

Abstract : Dimensionality reduction can be efficiently achieved by generative latent variable models such as probabilistic principal component analysis PPCA or independent component analysis ICA, aiming to extract a reduced set of variables latent variables from the original ones. In most cases, the learning of these methods is achieved within the unsupervised framework where only unlabeled samples are used. In this paper we investigate the possibility of estimating independent factor analysis model IFA and thus projecting original data onto a lower dimensional space, when prior knowledge on the cluster membership of some training samples is incorporated. In the basic IFA model, latent variables are only recovered from their linear observed mixtures original features. Both the mapping matrix assumed to be linear and the latent variable densities that are assumed to be mutually independent and generated according to mixtures of Gaussians are learned from observed data. We propose to learn this model within semisupervised framework where the likelihood of both labeled and unlabeled samples is maximized by a generalized expectation-maximization GEM algorithm. Experimental results on real data sets are provided to demonstrate the ability of our approach to find law dimensional manifold with good explanatory power.

Keywords : Independent factor analysis semi-supervised learning mixture models maximum likelihood dimensionality reduction

Author: Latifa Oukhellou - Etienne Côme - Patrice Aknin - Thierry Denoeux -



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