Time-frequency learning machines

Abstract : Over the last decade, the theory of reproducing kernels has made a major breakthrough in the field of pattern recognition. It has led to new algorithms, with improved performance and lower computational cost, for non-linear analysis in high dimensional feature spaces. Our paper is a further contribution which extends the framework of the so-called kernel learning machines to time-frequency analysis, showing that some specific reproducing kernels allow these algorithms to operate in the time-frequency domain. This link offers new perspectives in the field of non-stationary signal analysis, which can benefit from the developments of pattern recognition and Statistical Learning Theory.
Type de document :
Pré-publication, Document de travail
12 pages, 5 figures. To appear in IEEE Transactions on Signal Processing. 2006
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Soumis le : jeudi 7 décembre 2006 - 09:34:22
Dernière modification le : jeudi 27 décembre 2018 - 15:40:06
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  • HAL Id : ensl-00118896, version 1



Paul Honeiné, Cédric Richard, Patrick Flandrin. Time-frequency learning machines. 12 pages, 5 figures. To appear in IEEE Transactions on Signal Processing. 2006. 〈ensl-00118896〉



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