SVM-like decision theoretical classification of high-dimensional vectors

    Authors

    D. J. Bradshaw;M. Pensky

    Comments

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    Abbreviated Journal Title

    J. Stat. Plan. Infer.

    Keywords

    Support vector machine; Decision theoretical approach; Posterior; probabilities; MACHINES; Statistics & Probability

    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. (C) 2009 Elsevier B.V. All rights reserved.

    Journal Title

    Journal of Statistical Planning and Inference

    Volume

    140

    Issue/Number

    3

    Publication Date

    1-1-2010

    Document Type

    Article

    Language

    English

    First Page

    705

    Last Page

    718

    WOS Identifier

    WOS:000272635800011

    ISSN

    0378-3758

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