Representing Functional Data Using Support Vector Machines
Functional data are difficult to manage for many traditional pattern recognition techniques, given the very high (or intrinsically infinite) dimensionality. The reason is that functional data are functions and most algorithms are designed to work with (small) finite-dimensional vectors. In this paper we propose a functional analysis technique to obtain finite-dimensional representations of functional data. The key idea is to consider each functional curve as a point in a general function space and then project these points onto a Reproducing Kernel Hilbert Space with the aid of a Support Vector Machine. We show some theoretical properties of the method and illustrate the performance of the proposed representation in clustering using a real data set.
KeywordsSupport Vector Machines Reproducing Kernel Hilbert Spaces Functional Data