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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.
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Submitted on : Thursday, April 25, 2019 - 11:12:57 PM
Last modification on : Wednesday, January 26, 2022 - 11:05:26 AM


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Paul Honeine, Cédric Richard, Patrick Flandrin. Time-Frequency Learning Machines. IEEE Transactions on Signal Processing, Institute of Electrical and Electronics Engineers, 2007, 55 (7), pp.3930-3936. ⟨10.1109/TSP.2007.894252⟩. ⟨hal-02111309⟩



Les métriques sont temporairement indisponibles