 |
Conference papers
Filter bank Kernel Learning for nonstationary signal classification
Abstract : This paper addresses the problem of automatic feature extraction for signal classification. In order to handle non-stationarity, features are designed in the time-frequency domain using a Filter Bank as the mapping function, which enables an easy interpretation for practitioners. The strategy adopted is to jointly learn a Filter Bank with a Support Vector Machine by casting the optimization program as a Multiple Kernel Learning problem. This solves the program for a finite set of filters. Thus, in order to handle an infinite number of filters, a novel active constraint algorithm is proposed based on the latest breakthroughs. Our method has been tested on a toy dataset and compared to classical methods with competitive results.
https://hal-cea.archives-ouvertes.fr/cea-01830770
Contributor : Léna Le Roy Connect in order to contact the contributor
Submitted on : Thursday, July 5, 2018 - 1:12:48 PM
Last modification on : Friday, October 22, 2021 - 4:41:04 AM
Contributor : Léna Le Roy Connect in order to contact the contributor
Submitted on : Thursday, July 5, 2018 - 1:12:48 PM
Last modification on : Friday, October 22, 2021 - 4:41:04 AM