Simple Incremental One-Class Support Vector Classification

Authors:
Kai Labusch;Fabian Timm;Thomas Martinetz
Affiliations:
Institute for Neuro- and Bioinformatics, University of Lübeck, Lübeck, Germany D-23538;Institute for Neuro- and Bioinformatics, University of Lübeck, Lübeck, Germany D-23538;Institute for Neuro- and Bioinformatics, University of Lübeck, Lübeck, Germany D-23538
Venue:
Proceedings of the 30th DAGM symposium on Pattern Recognition
Year:
2008

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Abstract

We introduce the OneClassMaxMinOver (OMMO) algorithm for the problem of one-class support vector classification. The algorithm is extremely simple and therefore a convenient choice for practitioners. We prove that in the hard-margin case the algorithm converges with $\mathcal{O} (1/\sqrt{t})$ to the maximum margin solution of the support vector approach for one-class classification introduced by Schölkopf et al. Furthermore, we propose a 2-norm soft margin generalisation of the algorithm and apply the algorithm to artificial datasets and to the real world problem of face detection in images. We obtain the same performance as sophisticated SVM software such as libSVM.