Abstract

A Bayesian SOM (BSOM) [8], is proposed and applied to the unsupervised learning of
Gaussian mixture distributions and its performance is compared with the expectation-maximisation
(EM) algorithm. The BSOM is found to yield as good results as the well-known EM algorithm but
with much fewer iterations and, more importantly it can be used as an on-line training method. The
neighbourhood function and distance measures of the traditional SOM [3] are replaced by the neuron's
on-line estimated posterior probabilities, which can be interpreted as a Bayesian inference of the
neuron's opportunity to share in the winning response and so to adapt to the input pattern. Such
posteriors starting from uniform priors are gradually sharpened when more and more data samples
become available and so improve the estimation of model parameters. Each neuron then converges to
one component of the mixture. Experimental results are compared with those of the EM algorithm.