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Local Entropies for Kernel Selection and Outlier Detection in Functional Data

  • Gabriel MartosEmail author
  • Alberto Muñoz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9423)

Abstract

An important question in data analysis is how to choose the kernel function (or its parameters) to solve classification or regression problems. The choice of a suitable kernel is usually carried out by cross validation. In this paper we introduce a novel method consisting in choosing the kernel according to an optimal entropy criterion. After selecting the best kernel function we proceed by using a measure of local entropy to compute the functional outliers in the sample.

Keywords

Local entropy Functional data Kernel selection Outlier detection 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of StatisticsUniversity Carlos IIIMadridSpain

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