Noiseless Independent Factor Analysis with mixing constraints in a semi-supervised framework. Application to railway device fault diagnosis.

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 gener- ated 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 diag- nosis 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.
Complete list of metadatas

Cited literature [15 references]  Display  Hide  Download

https://hal-paris1.archives-ouvertes.fr/hal-00446628
Contributor : Etienne Côme <>
Submitted on : Wednesday, January 13, 2010 - 12:25:41 PM
Last modification on : Wednesday, June 26, 2019 - 9:56:02 AM
Long-term archiving on : Thursday, June 17, 2010 - 10:43:06 PM

File

icann.pdf
Files produced by the author(s)

Identifiers

Citation

Etienne Côme, Latifa Oukhellou, Patrice Aknin, Thierry Denoeux. Noiseless Independent Factor Analysis with mixing constraints in a semi-supervised framework. Application to railway device fault diagnosis.. International Conference on Artificial Neural Networks (ICANN),, Sep 2009, Limassol, Cyprus. pp.416-425, ⟨10.1007/978-3-642-04277-5_42⟩. ⟨hal-00446628⟩

Share

Metrics

Record views

321

Files downloads

266