Adaptive classification based on compressed data using learning vector quantization

Author

Baras, John S. ; Dey, Subhrakanti

Author_Institution

Dept. of Electr. & Comput. Eng., Maryland Univ., College Park, MD, USA

Volume

4

fYear

1999

fDate

6/21/1905 12:00:00 AM

Firstpage

3677

Abstract

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

Keywords

adaptive systems; approximation theory; convergence; data compression; differential equations; learning (artificial intelligence); pattern classification; vector quantisation; ATR; LVQ; ODE method; adaptive classification; aided target recognition; compressed data; convergence; fault detection; fault identification; learning VQ; learning vector quantization; manufacturing systems; medical diagnosis; 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

Publisher

ieee

Conference_Titel

Decision and Control, 1999. Proceedings of the 38th IEEE Conference on

Conference_Location

Phoenix, AZ

ISSN

0191-2216

Print_ISBN

0-7803-5250-5

Type

conf

DOI

10.1109/CDC.1999.827925

Filename

827925