Dynamic topology learning with the probabilistic self-organizing graph

Abstract

Self-organizing neural networks are usually focused on prototype learning, while the topology is held fixed during the learning process. Here a method to adapt the topology of the network so that it reflects the internal structure of the input distribution is proposed. This leads to a self-organizing graph, where each unit is a mixture component of a mixture of Gaussians (MoG). The corresponding update equations are derived from the stochastic approximation framework. This approach combines the advantages of probabilistic mixtures with those of self-organization. Experimental results are presented to show the self-organization ability of our proposal and its performance when used with multivariate datasets in classification and image segmentation tasks.