Unsupervised data classification using pairwise Markov chains with automatic copulas selection

Authors:
StéPhane Derrode;Wojciech Pieczynski
Affiliations:
Institut Fresnel (CNRS UMR 7249), Aix*Marseilles University and ícole Centrale Marseille, 38, rue Frédéric Joliot-Curie, 13451 Marseille cedex 20, France;CITI Department (CNRS UMR 5157), TELECOM SudParis, Institut Télécom 9, Rue Charles Fourier, 91011 Evry cedex, France
Venue:
Computational Statistics & Data Analysis
Year:
2013

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Abstract

The Pairwise Markov Chain (PMC) model assumes the couple of observations and states processes to be a Markov chain. To extend the modeling capability of class-conditional densities involved in the PMC model, copulas are introduced and the influence of their shape on classification error rates is studied. In particular, systematic experiments show that the use of wrong copulas can degrade significantly classification performances. Then an algorithm is presented to identify automatically the right copulas from a finite set of admissible copulas, by extending the general ''Iterative Conditional Estimation'' (ICE) parameters estimation method to the context considered. The unsupervised segmentation of a radar image illustrates the nice behavior of the algorithm.