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
Copyright Status
Unknown
Socpus ID
41549084260 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/41549084260
STARS Citation
Bradshaw, David and Pensky, Marianna, "Decision Theory Classification Of High-Dimensional Vectors Based On Small Samples" (2008). Scopus Export 2000s. 9859.
https://stars.library.ucf.edu/scopus2000/9859