Operator-valued Kernels for Learning from Functional Response Data - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Journal of Machine Learning Research Année : 2016

Operator-valued Kernels for Learning from Functional Response Data

Résumé

In this paper we consider the problems of supervised classification and regression in the case where attributes and labels are functions: a data is represented by a set of functions, and the label is also a function. We focus on the use of reproducing kernel Hilbert space theory to learn from such functional data. Basic concepts and properties of kernel-based learning are extended to include the estimation of function-valued functions. In this setting, the representer theorem is restated, a set of rigorously defined infinite-dimensional operator-valued kernels that can be valuably applied when the data are functions is described, and a learning algorithm for nonlinear functional data analysis is introduced. The methodology is illustrated through speech and audio signal processing experiments.
Fichier principal
Vignette du fichier
kadri15a.pdf (1.15 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-01221329 , version 1 (27-10-2015)
hal-01221329 , version 2 (29-10-2015)

Identifiants

Citer

Hachem Kadri, Emmanuel Duflos, Philippe Preux, Stéphane Canu, Alain Rakotomamonjy, et al.. Operator-valued Kernels for Learning from Functional Response Data. Journal of Machine Learning Research, 2016, 17 (20), pp.1-54. ⟨hal-01221329v2⟩
469 Consultations
575 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More