An unsupervised learning rule for class discrimination in a recurrent neural network

Abstract

A number of well-known unsupervised feature extraction neural network models are present in literature. The development of unsupervised pattern classification systems, although they share many of the principles of the aforementioned network models, has proven to be more elusive. This paper describes in detail a neural network capable of performing class separability through self-organizing Hebbian like dynamics, i.e., the network is able to autonomously find classes of patterns without the help from any external agent. The model is built around a recurrent network performing winner-takes-all competition. Automatic: labelling of input data samples is based upon the induced activity pattern after presentation of the sample. Neurons compete against each other through recurrent interactions to code the input sample. Resulting active neurons update their parameters to improve the classification process. Tho learning dynamics are moreover absolutely stable. © Springer-Vorlag Berlin Heidelberg 2000.