Title

Svm-Like Decision Theoretical Classification Of High-Dimensional Vectors

Keywords

Decision theoretical approach; Posterior probabilities; Support vector machine

Abstract

In this paper, we consider the classification of high-dimensional vectors based on a small number of training samples from each class. The proposed method follows the Bayesian paradigm, and it is based on a small vector which can be viewed as the regression of the new observation on the space spanned by the training samples. The classification method provides posterior probabilities that the new vector belongs to each of the classes, hence it adapts naturally to any number of classes. Furthermore, we show a direct similarity between the proposed method and the multicategory linear support vector machine introduced in Lee et al. [2004. Multicategory support vector machines: theory and applications to the classification of microarray data and satellite radiance data. Journal of the American Statistical Association 99 (465), 67-81]. We compare the performance of the technique proposed in this paper with the SVM classifier using real-life military and microarray datasets. The study shows that the misclassification errors of both methods are very similar, and that the posterior probabilities assigned to each class are fairly accurate. © 2009 Elsevier B.V. All rights reserved.

Publication Date

3-1-2010

Publication Title

Journal of Statistical Planning and Inference

Volume

140

Issue

3

Number of Pages

705-718

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/j.jspi.2009.09.001

Socpus ID

70350728401 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/70350728401

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