Combined Compression and Classification with Learning Vector Quantization
Date: September 01 - September 01, 1999
Combined compression and classification problems are becoming increasingly important in many applications with large amounts of sensory data and large sets of classes. These applications range from automatic target recognition (ATR) to medical diagnosis, speech recognition, and fault detection and identification in manufacturing systems. In this paper, we develop and analyze a learning vector quantization (LVQ) based algorithm for combined compression and classification. We show convergence of the algorithm using the ODE method from stochastic approximation. We illustrate the performance of our algorithm with some examples.