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Optimal Selection of Time-Frequency Representations for Signal Classification: a Kernel-Target Alignment Approach

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4 Author(s)
P. Honeine ; Sonalyse, Pist Oasis, 131 impasse des palmiers, 30319 Alès, France ; C. Richard ; P. Flandrin ; J. -B. Pothin

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

Published in:

2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings  (Volume:3 )

Date of Conference:

14-19 May 2006