Representing Functional Data using Support Vector Machines Articles uri icon

authors

  • MUÑOZ CUENCA, ALBERTO
  • GONZALEZ GARCIA, JAVIER

publication date

  • April 2010

start page

  • 511

end page

  • 516

issue

  • 6

volume

  • 31

International Standard Serial Number (ISSN)

  • 0167-8655

Electronic International Standard Serial Number (EISSN)

  • 1872-7344

abstract

  • Functional data are difficult to manage for most classical statistical techniques, given the very high (or intrinsically infinite) dimensionality. The reason lies in that functional data are functions
    and most algorithms are designed to work with low 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 datum as a point in a general function space
    and then to 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 its performance in some
    classification examples.