Multiple Operator-valued Kernel Learning - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2012

Multiple Operator-valued Kernel Learning


Positive definite operator-valued kernels generalize the well-known notion of reproducing kernels, and are naturally adapted to multi-output learning situations. This paper addresses the problem of learning a finite linear combination of infinite-dimensional operator-valued kernels which are suitable for extending functional data analysis methods to nonlinear contexts. We study this problem in the case of kernel ridge regression for functional responses with an lr-norm constraint on the combination coefficients. The resulting optimization problem is more involved than those of multiple scalar-valued kernel learning since operator-valued kernels pose more technical and theoretical issues. We propose a multiple operator-valued kernel learning algorithm based on solving a system of linear operator equations by using a block coordinatedescent procedure. We experimentally validate our approach on a functional regression task in the context of finger movement prediction in brain-computer interfaces.
Fichier principal
Vignette du fichier
RR-7900.pdf (297.52 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-00677012 , version 1 (07-03-2012)
hal-00677012 , version 2 (14-06-2012)



Hachem Kadri, Alain Rakotomamonjy, Francis Bach, Philippe Preux. Multiple Operator-valued Kernel Learning. Neural Information Processing Systems (NIPS), Dec 2012, Lake Tahoe, United States. ⟨hal-00677012v2⟩
493 View
693 Download



Gmail Facebook X LinkedIn More