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Filter bank learning for signal classification

Maxime Sangnier 1 J. Gauthier 1 A. Rakotomamonjy 2
1 LADIS - Laboratoire d'analyse des données et d'intelligence des systèmes
DM2I - Département Métrologie Instrumentation & Information : DRT/LIST/DM2I
2 DocApp - LITIS - Equipe Apprentissage
LITIS - Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes
Abstract : This paper addresses the problem of feature extraction for signal classification. It proposes to build features by designing a data-driven filter bank and by pooling the time-frequency representation to provide time-invariant features. For this purpose, our work tackles the problem of jointly learning the filters of a filter bank with a support vector machine. It is shown that, in a restrictive case (but consistent to prevent overfitting), the problem boils down to a multiple kernel learning instance with infinitely many kernels. To solve such a problem, we build upon existing methods and propose an active constraint algorithm able to handle a non-convex combination of an infinite number of kernels. Numerical experiments on both a brain-computer interface dataset and a scene classification problem prove empirically the appeal of our method.
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Contributor : Marie-France Robbe <>
Submitted on : Thursday, August 30, 2018 - 5:06:02 PM
Last modification on : Saturday, May 1, 2021 - 3:48:32 AM



Maxime Sangnier, J. Gauthier, A. Rakotomamonjy. Filter bank learning for signal classification. Signal Processing, Elsevier, 2015, 113, pp.124 - 137. ⟨10.1016/j.sigpro.2014.12.028⟩. ⟨cea-01865050⟩



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