Local Entropies for Kernel Selection and Outlier Detection in Functional Data

摘要

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.