Growing Neural Gas with Correntropy Induced Metric

Abstract

This paper discusses the Correntropy Induced Metric (CIM) based Growing Neural Gas (GNG) architecture. CIM is a kernel method based similarity measurement from the information theoretic learning perspective, which quantifies the similarity between probability distributions of input and reference vectors. We apply CIM to find a maximum error region and node insert criterion, instead of euclidean distance based function in original GNG. Furthermore, we introduce the two types of Gaussian kernel bandwidth adaptation methods for CIM. The simulation experiments in terms of the affect of kernel bandwidth σ in CIM, the self-organizing ability, and the quantitative comparison show that proposed model has the superior abilities than original GNG.