Estimation de signaux par noyaux d'ondelettes

Estimation de signaux par noyaux d'ondelettes

Estimating signals using multiple wavelet kernels

Vincent Guigue Alain Rakotomamonjy  Stéphane Canu 

Laboratoire Perception, Systèmes, Information, avenue de l'Université, 76801 St Étienne du Rouvray

Corresponding Author Email:
14 October 2005
31 December 2006
| Citation



This paper addresses the problem of regression in the case of non-uniform sampled signals. Our method is based on supervised learning theory, we propose to use L2 estimation with wavelet kernels combined with L1 multiscale regularization. The use of Least Angle Regression as solver enable us to propose new solutions to set the regularization parameter.


Cet article présente une méthode de régression pour les signaux non uniformément échantillonnés basée sur les ondelettes. Nous utilisons une formulation issue de l'apprentissage supervisé et des méthodes à noyaux qui combine une fonction coût L2 et une régularisation L1 multi-échelles. L'utilisation de l'algorithme Least Angle Regression pour la résolution du problème est à la fois efficace et intéressante, elle permet de calculer le chemin complet de régularisation et d'introduire de nouvelles solutions pour régler le compromis biais-variance.


Regularization L1, Multiple Kernels, Wavelets, Regression

Mots clés

Régularisation L1, Noyaux multiples, Ondelettes, Régression

1. Introduction
2. Méthode
3. Réglage Du Compromis Biais-Variance
4. Résultats
5. Conclusions

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