General Framework for Nonlinear Functional Regression with Reproducing Kernel Hilbert Spaces
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.
Domaines
Machine Learning [stat.ML]| Origine | Accord explicite pour ce dépôt |
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