Context Quantization and Contextual Self-Organizing Maps

Vector quantization consists in finding a discrete approximation of a continuous input. One of the most popular neural algorithms related to vector quantization is the, so-called, Kohonen map. In this paper, we generalize vector quantization to temporal data, introducing context quantization. We propose a recurrent network inspired by the Kohonen map, the Contextual Self-Organizing Map that develops near-optimal representations of context. We demonstrate quantitatively that this algorithm shows better performance than the other neural methods proposed so far.