Semi-supervised IFA with Prior Knowledge on the Mixing Process: An Application to a Railway Device Diagnosis

Independent factor analysis (IFA) is a well known method used to recover independent components from their linear observed mixtures without any knowledge on the mixing process. Such recovery is possible thanks to the hypothesis that the components are mutually independent and non-Gaussians. The IFA model assumes furthermore that each component is distributed according to a mixture of Gaussians. This article investigates the possibility of incorporating prior knowledge on the mixing process and partial knowledge on the cluster belonging of some samples to estimate the IFA model. In this way, other learning contexts can be handled such as semi-supervised or partially supervised learning. Such information is valuable to enhance estimation accuracy and remove indeterminacy commonly encountered in unsupervised IFA such as the permutation of the sources. The proposed method is illustrated by a railway device diagnosis application and results are provided to show its effectiveness for this type of problem.