A regression model with a hidden logistic process for signal parametrization

Abstract

A new approach for signal parametrization, which consists of a specific regression model incorporating a discrete hidden logistic process, is proposed. The model parameters are estimated by the maximum likelihood method performed by a dedicated Expectation Maximization (EM) algorithm. The parameters of the hidden logistic process, in the inner loop of the EM algorithm, are estimated using a multi-class Iterative Reweighted Least-Squares (IRLS) algorithm. An experimental study using simulated and real data reveals good performances of the proposed approach.

In Proceedings of the XVIIth European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Pages 503-508, 2009, Bruges, Belgium

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