Local Entropies for Kernel Selection and Outlier Detection in Functional Data

Gabriel Martos, Alberto Muñoz

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.