Title

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

Authors

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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