Building smooth neighbourhood kernels via functional data analysis

  • Authors:

  • Alberto Muñoz;Javier M. Moguerza

  • Affiliations:

  • University Carlos III, Getafe, Spain;University Rey Juan Carlos, Móstoles, Spain

  • Venue:
  • ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
  • Year:
  • 2005

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Abstract

In this paper we afford the problem of estimating high density regions from univariate or multivariate data samples. To be more precise, we propose a method based on the use of functional data analysis techniques for the construction of smooth kernel functions oriented to solve the One-Class problem. The proposed kernels increase the precision of One-Class estimation procedures. The advantages of this new point of view are shown using data sets drawn from representative density functions.