Building smooth neighbourhood kernels via functional data analysis

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. © Springer-Verlag Berlin Heidelberg 2005.