Clustering with multilayer perceptrons and hebbian learning based on kullback-leibler divergence
J.R. Montalvho Filho, M.A. Bezerra, Liziane Paixão Silva Oliveira · 2005
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 presented and illustrated by practical experiments