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Multiple Operator-valued Kernel Learning

Hachem Kadri 1 Alain Rakotomamonjy 2 Francis Bach 3, 4 Philippe Preux 1
1 SEQUEL - Sequential Learning
LIFL - Laboratoire d'Informatique Fondamentale de Lille, LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal, Inria Lille - Nord Europe
3 SIERRA - Statistical Machine Learning and Parsimony
DI-ENS - Département d'informatique de l'École normale supérieure, ENS Paris - École normale supérieure - Paris, Inria Paris-Rocquencourt, CNRS - Centre National de la Recherche Scientifique : UMR8548
Abstract : This paper addresses the problem of learning a finite linear combination of operator-valued kernels. We study this problem in the case of kernel ridge regression for functional responses with a lr-norm constraint on the combination coefficients. We propose a multiple operator-valued kernel learning algorithm based on solving a system of linear operator equations by using a block coordinate descent procedure. We experimentally validate our approach on a functional regression task in the context of finger movement prediction in Brain-Computer Interface (BCI).
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Contributor : Hachem Kadri <>
Submitted on : Wednesday, March 7, 2012 - 8:55:40 AM
Last modification on : Friday, November 1, 2019 - 4:46:06 PM
Document(s) archivé(s) le : Friday, June 8, 2012 - 2:20:56 AM


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  • HAL Id : hal-00677012, version 1
  • ARXIV : 1203.1596



Hachem Kadri, Alain Rakotomamonjy, Francis Bach, Philippe Preux. Multiple Operator-valued Kernel Learning. [Research Report] RR-7900, 2012. ⟨hal-00677012v1⟩



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