HAL - INRIA :: [inria-00378381, version 1] General Framework for Nonlinear Functional Regression with Reproducing Kernel Hilbert Spaces

inria-00378381, version 1

General Framework for Nonlinear Functional Regression with Reproducing Kernel Hilbert Spaces

Hachem Kadri ()^a^, 1, Emmanuel Duflos ()^b^, 1, Manuel Davy ()^a^, 1, Philippe Preux ()^c^, 1, Stephane Canu ²

N° RR-6908 (2009)

Résumé : In this paper, we discuss concepts and methods of nonlinear regression for functional data. The focus is on the case where covariates and responses are functions. We present a general framework for modelling functional regression problem in the Reproducing Kernel Hilbert Space (RKHS). Basics concepts of kernel regression analysis in the real case are extended to the domain of functional data analysis. Our main results show how using Hilbert spaces theory to estimate a regression function from observed functional data. This procedure can be thought of as a generalization of scalar-valued nonlinear regression estimate.

a – CNRS
b – Ecole Centrale de Lille
c – Université Charles de Gaulle - Lille III
1 : SEQUEL (INRIA Futurs)
INRIA – CNRS : UMR8022 – CNRS : UMR8146 – Université des Sciences et Technologies de Lille - Lille I – Université Charles de Gaulle - Lille III – Ecole Centrale de Lille
2 : Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes (LITIS)
Institut National des Sciences Appliquées de Rouen – Université du Havre – Université de Rouen : EA4108

inria-00378381, version 1
http://hal.inria.fr/inria-00378381
oai:hal.inria.fr:inria-00378381
Contributeur : Hachem Kadri <>
Soumis le : Vendredi 24 Avril 2009, 10:48:02
Dernière modification le : Samedi 25 Avril 2009, 12:39:16

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