Support vector domain description (1999)
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| Venue: | Pattern Recognition Letters |
| Citations: | 177 - 8 self |
BibTeX
@ARTICLE{Tax99supportvector,
author = {David M. J. Tax and Robert P. W. Duin},
title = {Support vector domain description},
journal = {Pattern Recognition Letters},
year = {1999},
volume = {20},
pages = {1191--1199}
}
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Abstract
This paper shows the use of a data domain description method, inspired by the support vector machine by Vapnik, called the support vector domain description (SVDD). This data description can be used for novelty or outlier detection. A spherically shaped decision boundary around a set of objects is constructed by a set of support vectors describing the sphere boundary. It has the possibility of transforming the data to new feature spaces without much extra computational cost. By using the transformed data, this SVDD can obtain more ¯exible and more accurate data descriptions. The error of the ®rst kind, the fraction of the training objects which will be rejected, can be estimated immediately from the description without the use of an independent test set, which makes this method data e cient. The support vector domain description is compared with other outlier detection methods on real data. Ó 1999 Elsevier Science B.V. All rights reserved. Keywords: Data domain description; Outlier detection; One-class classi®cation; Support vector machines
Keyphrases
support vector domain description support vector machine one-class classi cation method data much extra computational cost training object shaped decision boundary independent test set outlier detection elsevier science b.v. right data domain description method detection method real data accurate data description rst kind data domain description data description support vector sphere boundary new feature
