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

Volume

45

Issue

6

fYear

1999

fDate

9/1/1999 12:00:00 AM

Firstpage

1911

Lastpage

1920

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;

fLanguage

English

Journal_Title

Information Theory, IEEE Transactions on

Publisher

ieee

ISSN

0018-9448

Type

jour

DOI

10.1109/18.782112

Filename

782112