A regression model with a hidden logistic process for signal parametrization
Methodology
2013-12-30 v1 Machine Learning
Machine Learning
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.
Cite
@article{arxiv.1312.6994,
title = {A regression model with a hidden logistic process for signal parametrization},
author = {Faicel Chamroukhi and Allou Samé and Gérard Govaert and Patrice Aknin},
journal= {arXiv preprint arXiv:1312.6994},
year = {2013}
}
Comments
In Proceedings of the XVIIth European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Pages 503-508, 2009, Bruges, Belgium