A hidden process regression model for functional data description. Application to curve discrimination
Methodology
2013-12-30 v1 Machine Learning
Machine Learning
Abstract
A new approach for functional data description is proposed in this paper. It consists of a regression model with a discrete hidden logistic process which is adapted for modeling curves with abrupt or smooth regime changes. The model parameters are estimated in a maximum likelihood framework through a dedicated Expectation Maximization (EM) algorithm. From the proposed generative model, a curve discrimination rule is derived using the Maximum A Posteriori rule. The proposed model is evaluated using simulated curves and real world curves acquired during railway switch operations, by performing comparisons with the piecewise regression approach in terms of curve modeling and classification.
Cite
@article{arxiv.1312.6968,
title = {A hidden process regression model for functional data description. Application to curve discrimination},
author = {Faicel Chamroukhi and Allou Samé and Gérard Govaert and Patrice Aknin},
journal= {arXiv preprint arXiv:1312.6968},
year = {2013}
}