Estimation de signaux par noyaux d’ondelettes
Vincent Guigue, Alain Rakotomamonjy and Stéphane Canu
In: GRETSI 2005, 6-9 Sept. 2005, Louvain la neuve, Belgium.

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Abstract

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 kernel combined with L1 multiscale regularization. The use of Least Angle Regression as solver enable us to propose new solutions to set the regularization parameter.

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