Title :
Combined compression and classification with learning vector quantization
Author :
Baras, John S. ; Dey, Subhrakanti
Author_Institution :
Dept. of Electr. & Comput. Eng., Maryland Univ., College Park, MD, USA
fDate :
9/1/1999 12:00:00 AM
Abstract :
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 the algorithm with some examples
Keywords :
approximation theory; cepstral analysis; convergence of numerical methods; data compression; learning systems; signal classification; speech coding; speech recognition; stochastic processes; vector quantisation; LVQ based algorithm; ODE method; algorithm convergence; automatic target recognition; data classification; data compression; fault detection; identification; learning vector quantization; manufacturing systems; medical diagnosis; mel-cepstrum coefficients; performance; sensory data; simulation results; speech recognition; stochastic approximation; Algorithm design and analysis; Approximation algorithms; Convergence; Fault detection; Fault diagnosis; Manufacturing systems; Medical diagnosis; Speech recognition; Target recognition; Vector quantization;
Journal_Title :
Information Theory, IEEE Transactions on
