Clustering with multilayer perceptrons and self-organized (Hebbian) learning

Abstract

A new local (Hebbian) learning algorithm for artificial neurons is presented. It is shown that, in spite of its implementation simplicity, this new algorithm, applied to neurons with sigmoidal activation function, performs data clustering by finding valleys of the probability density function (PDF) of the multivariate random variables that model incoming data. Some interesting features of this new algorithm are illustrated by some experiments based on both artificial data and real world data.