Supervised VQ learning based on temporal inhibition

Abstract

In the context of supervised vectorial quantization (VQ) learning algorithms, we present an algorithm (SLTI) that exploits the self-organizing properties arising from a particular process of temporal inhibition of the winning units in competitive learning. This exploitation consists of establishing independence capabilities in the initialization of the prototypes (weight vectors), together with generalization capabilities, which to a certain extent solve some of the critical problems involved in the use of conventional algorithms such as LVQs and DSM. Another original aspect of this paper is the inclusion in SLTI of a simple rule for prototype adaptation, which incorporates certain useful features that make possible to plan the configuration of the SLTI parameters with specific goals in order to approach classification tasks of varied complexity and natures (versatility). This versatility is experimentally demonstrated with synthetic data comprising non linearly-separable classes, overlapping classes and interlaced classes with a certain degree of overlap.