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    Combined Compression and Classification with Learning Vector Quantization


    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:

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