Filter bank Kernel Learning for nonstationary signal classification
| Autor: | Maxime Sangnier, Jerome Gauthier, Alain Rakotomamonjy |
|---|---|
| Přispěvatelé: | Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes (LITIS), Université Le Havre Normandie (ULH), Normandie Université (NU)-Normandie Université (NU)-Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA), Laboratoire Outils d'Analyse des Données (LOAD), Département Métrologie Instrumentation & Information (DM2I), Laboratoire d'Intégration des Systèmes et des Technologies (LIST (CEA)), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Laboratoire d'Intégration des Systèmes et des Technologies (LIST (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Direction Générale de l’Armement, Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes (LITIS), Laboratoire d'analyse des données et d'intelligence des systèmes (LADIS), Laboratoire d'Intégration des Systèmes et des Technologies (LIST), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Laboratoire d'Intégration des Systèmes et des Technologies (LIST) |
| Jazyk: | angličtina |
| Rok vydání: | 2013 |
| Předmět: |
Graph kernel
Computer science Filter banks SVM Feature extraction Linear classifier 02 engineering and technology 010501 environmental sciences 01 natural sciences Relevance vector machine Time-frequency representations [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing [STAT.ML]Statistics [stat]/Machine Learning [stat.ML] Polynomial kernel Least squares support vector machine 0202 electrical engineering electronic engineering information engineering Kernel adaptive filter signal processing 0105 earth and related environmental sciences Active filters Multiple kernel learning Support vector machines Time frequency domain business.industry Automatic feature extraction Nonstationary signals 020206 networking & telecommunications Pattern recognition Filter bank Signal classification artificial intelligence Support vector machine Optimization programs Kernel method machine learning classification Kernel embedding of distributions [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR] Radial basis function kernel Multiple Kernel Learning Artificial intelligence Tree kernel business Algorithm [STAT.ME]Statistics [stat]/Methodology [stat.ME] |
| Zdroj: | IEEE Xplore ICASSP 2013-IEEE International Conference on Acoustics, Speech and Signal Processing ICASSP 2013-IEEE International Conference on Acoustics, Speech and Signal Processing, May 2013, Vancouver, BC, Canada. pp.3183-3187, ⟨10.1109/ICASSP.2013.6638245⟩ ICASSP 2013 IEEE International Conference on Acoustics, Speech and Signal Processing 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, May 2013, Vancouver, BC, Canada. pp.3183-3187, ⟨10.1109/ICASSP.2013.6638245⟩ |
| DOI: | 10.1109/ICASSP.2013.6638245⟩ |
| Popis: | Conference of 2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, (ICASSP 2013) ; Conference Date: 26 May 2013 Through 31 May 2013; Conference Code:101421; International audience; 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. |
| Databáze: | OpenAIRE |
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