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Article Dans Une Revue Pattern Analysis and Applications Année : 2012

Fault diagnosis of a railway device using semi-supervised independent factor analysis with mixing constraints

Résumé

Independent factor analysis (IFA) defines a generative model for observed data that are assumed to be linear mixtures of some unknown non-Gaussian, mutually independent latent variables (also called sources or independent components). The probability density function of each individual latent variable is modelled by a mixture of Gaussians (MOG). Learning in the context of this model is usually performed within an unsupervised framework in which only unlabelled samples are used. Both the mixing matrix and the parameters of latent variable densities are learned from observed data. This paper investigates the possibility of estimating an IFA model in a noiseless setting when two kinds of prior information are incorporated; namely, constraints on the mixing process and partial knowledge on the cluster membership of some training samples. Semi-supervised or partially-supervised learning frameworks can thus be handled. The investigation of these two kinds of prior information was motivated by a real-world application concerning the fault diagnosis of railway track circuits. Results from both this application, simulated data are provided to demonstrate the capacity of our approach to enhance estimation accuracy and remove the indeterminacy commonly encountered in unsupervised IFA, such as source permutations.
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Dates et versions

hal-00750589 , version 1 (11-11-2012)

Identifiants

Citer

Etienne Côme, Latifa Oukhellou, Thierry Denœux, Patrice Aknin. Fault diagnosis of a railway device using semi-supervised independent factor analysis with mixing constraints. Pattern Analysis and Applications, 2012, 15 (3), pp.313-326. ⟨10.1007/s10044-011-0212-3⟩. ⟨hal-00750589⟩
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