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Paper Detail

Paper:	SPTM-P6.7
Session:	Non-stationary Signals and Time-Frequency Analysis
Time:	Wednesday, May 17, 16:30 - 18:30
Presentation:	Poster
Topic:	Signal Processing Theory and Methods: Non-stationary Signals and Time-Frequency Analysis
Title:	Optimal selection of time-frequency representations for signal classification: a kernel-target alignment approach
Authors:	Paul Honeiné, Sonalyse, France; Cédric Richard, ISTIT (FRE CNRS 2732) / Troyes University of Technology, France; Patrick Flandrin, Laboratoire de Physique (UMR CNRS 5672) ENS Lyon, France; Jean-Baptiste Pothin, ISTIT (FRE CNRS 2732) / Troyes University of Technology, France
Abstract:	In this paper, we propose a method for selecting time-frequency distributions appropriate for given learning tasks. It is based on a criterion that has recently emerged from the machine learning literature: the kernel-target alignment. This criterion makes possible to find the optimal representation for a given classification problem without designing the classifier itself. Some possible applications of our framework are discussed. The first one provides a computationally attractive way of adjusting the free parameters of a distribution to improve classification performance. The second one is related to the selection, from a set of candidates, of the distribution that best facilitates a classification task. The last one addresses the problem of optimally combining several distributions.