Differential competitive learning for centroid estimation and phoneme recognition

A comparison is made of a differential-competitive-learning (DCL) system with two supervised competitive-learning (SCL) systems for centroid estimation and for phoneme recognition. DCL provides a form of unsupervised adaptive vector quantization. Standard stochastic competitive-learning systems learn only if neurons win a competition for activation induced by randomly sampled patterns. DCL systems learn only if the competing neurons change their competitive signal. Signal-velocity information provides unsupervised local reinforcement during learning. The sign of the neuronal signal derivative rewards winners and punishes losers. Standard competitive learning ignores instantaneous win-rate information. Synaptic fan-in vectors adaptively quantize the randomly sampled pattern space into nearest-neighbor decision classes. More generally, the synaptic-vector distribution estimates the unknown sampled probability density function p( x). Simulations showed that unsupervised DCL-trained synaptic vectors converged to class centroids at least as fast as, and wandered less about these centroids than, SCL-trained synaptic vectors did. Simulations on a small set of English phonemes favored DCL over SCL for classification accuracy