Combined Compression and Classification with Learning Vector Quantization

Abstract

Combined compression and classification problems are becoming increasinglyimportant in many applications with large amounts of sensory data andlarge sets of classes. These applications range from aided target recognition(ATR), to medicaldiagnosis, to speech recognition, to fault detection and identificationin manufacturing systems. In this paper, we develop and analyze a learningvector quantization-based (LVQ) algorithm for the combined compressionand classification problem. We show convergence of the algorithm usingtechniques from stochastic approximation, namely, the ODE method. Weillustrate the performance of our algorithm with some examples.