Noiseless Independent Factor Analysis with Mixing Constraints in a Semi-supervised Framework. Application to Railway Device Fault Diagnosis

Authors:
Etienne Côme;Latifa Oukhellou;Thierry Denœux;Patrice Aknin
Affiliations:
INRETS-LTN, Arcueil, France 94114;INRETS-LTN, Arcueil, France 94114 and Université Paris 12- CERTES, Créteil, France 94100;Heudiasyc, UTC - UMR CNRS 6599, Compiègne, France 60205;INRETS-LTN, Arcueil, France 94114
Venue:
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Year:
2009

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Abstract

In Independent Factor Analysis (IFA), latent components (or sources) are recovered from only their linear observed mixtures. Both the mixing process and the source densities (that are assumed to be generated according to mixtures of Gaussians) are learned from observed data. This paper investigates the possibility of estimating the IFA model in its noiseless setting when two kinds of prior information are incorporated: constraints on the mixing process and partial knowledge on the cluster membership of some examples. Semi-supervised or partially supervised learning frameworks can thus be handled. These two proposals have been initially motivated by a real-world application that concerns fault diagnosis of a railway device. Results from this application are provided to demonstrate the ability of our approach to enhance estimation accuracy and remove indeterminacy commonly encountered in unsupervised IFA such as source permutations.