A hidden process regression model for functional data description. Application to curve discrimination

Authors:
Faicel Chamroukhi;Allou Samé;Gérard Govaert;Patrice Aknin
Affiliations:
French National Institute for Transport and Safety Research (INRETS), Laboratory of New Technologies (LTN), 2 Rue de la Butte Verte, 93166 Noisy-le-Grand Cedex, France and Compiègne Universit ...;French National Institute for Transport and Safety Research (INRETS), Laboratory of New Technologies (LTN), 2 Rue de la Butte Verte, 93166 Noisy-le-Grand Cedex, France;Compiègne University of Technology, HEUDIASYC Laboratory, UMR CNRS 6599, BP 20529, 60205 Compiègne Cedex, France;French National Institute for Transport and Safety Research (INRETS), Laboratory of New Technologies (LTN), 2 Rue de la Butte Verte, 93166 Noisy-le-Grand Cedex, France
Venue:
Neurocomputing
Year:
2010

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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.