Adaptive Classification Based on Compressed Data Using Learning Vector Quantization
Baras, John, S.
Date: December 07 - December 10, 1999
Classification problems using compressed data are becoming increasingly important in many applications with large amounts of sensory data and large sets of classes. These applications range from Aided Target Recognition (ATR), to medical diagnosis, to speech recognition, to fault detection and identification in manufacturing systems. In this paper, we develop and analyze a learning vector quantization (LVQ) based algorithm for the combined compression and classification problem. We show convergence of the algorithm using techniques from stochastic approximation, namely, the ODE method.