Title

Decision Theory Classification Of High-Dimensional Vectors Based On Small Samples

Keywords

Decision theory; Matrix-variate normal distribution; Posterior probabilities; Support vector machine

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. © 2007 Sociedad de Estadística e Investigación Operativa.

Publication Date

5-1-2008

Publication Title

Test

Volume

17

Issue

1

Number of Pages

83-100

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1007/s11749-006-0024-8

Socpus ID

41549084260 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/41549084260

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