Vector Quantization for Multiple Classes

Abstract Vector quantization algorithms have long been used to find a finite set of exemplars which represent a data set to within an a priori error tolerance. Such a representation is essential in codebook-based data compression and transmission. The present study considers the situation where the data to be encoded consists of subclasses. The codebook must provide information compression within the several subclasses, however, minimization of interclass errors is of equal importance. We present modifications to a basic vector quanitization algorithm which adapts it to the multiclass vector quantizing setting. We then explore the behavior of the modified algorithm on selected benchmark applications. We show, in particular, that overlapping subclasses can be accommodated by the algorithm.