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Signal-Dependent Time-Frequency Representations for Classification using a Radially Gaussian Kernel and the Alignment Criterion

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2 Author(s)
Honeine, P. ; Institut Charles Delaunay (FRE CNRS 2848) - LM2S - Université de Technologie de Troyes, 12 rue Marie Curie, BP 2060, 10010 Troyes cedex, France - fax. +, (tel. + ; Richard, C.

In this paper, we propose a method for tuning time-frequency distributions with radially Gaussian kernel within a classification framework. It is based on a criterion that has recently emerged from the machine learning literature: the kernel-target alignement. Our optimization scheme is very similar to that proposed by Baraniuk and Jones for signal-dependent time-frequency analysis. The relevance of this approach of improving time-frequency classification accuracy is illustrated through examples.

Published in:

Statistical Signal Processing, 2007. SSP '07. IEEE/SP 14th Workshop on

Date of Conference:

26-29 Aug. 2007