Decision theory classification of high-dimensional vectors based on small samples

    Authors

    D. Bradshaw;M. Pensky

    Comments

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    Abbreviated Journal Title

    Test

    Keywords

    support vector machine; decision theory; posterior probabilities; matrix-variate normal distribution; MACHINES; RULE; Statistics & Probability

    Abstract

    In this paper, an entirely new procedure for the classification of high-dimensional vectors on the basis of a few training samples is described. The proposed method is based on the Bayesian paradigm and provides posterior probabilities that a new vector belongs to each of the classes, therefore it adapts naturally to any number of classes. The classification technique is based on a small vector which can be viewed as a regression of the new observation onto the space spanned by the training samples, which is similar to Support Vector Machine classification paradigm. This is achieved by employing matrix-variate distributions in classification, which is an entirely new idea.

    Journal Title

    Test

    Volume

    17

    Issue/Number

    1

    Publication Date

    1-1-2008

    Document Type

    Article

    Language

    English

    First Page

    83

    Last Page

    100

    WOS Identifier

    WOS:000254462600013

    ISSN

    1133-0686

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