Baras, John S. and Dey, Subhrakanti (1999) Combined Compression and Classification with Learning Vector Quantization. IEEE Transactions on Information Theory, 45 (6). pp. 1911-1920. ISSN 0018-9448

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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.

Item Type:	Article
Keywords:	Classification; compression; learning vector quantization; nonparametric; stochastic approximation;
Academic Unit:	Faculty of Science and Engineering > Electronic Engineering Faculty of Science and Engineering > Research Institutes > Hamilton Institute
Item ID:	14407
Identification Number:	https://doi.org/10.1109/18.782112
Depositing User:	Subhrakanti Dey
Date Deposited:	10 May 2021 14:37
Journal or Publication Title:	IEEE Transactions on Information Theory
Publisher:	IEEE
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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