A theoretical framework for multi-sphere support vector data description

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

13 Citations (Scopus)


In support vector data description (SVDD) a spherically shaped boundary around a normal data set is used to separate this set from abnormal data. The volume of this data description is minimized to reduce the chance of accepting abnormal data. However the SVDD does not guarantee that the single spherically shaped boundary can best describe the normal data set if there are some distinctive data distributions in this set. A better description is the use of multiple spheres, however there is currently no investigation available. In this paper, we propose a theoretical framework to multi-sphere SVDD in which an optimisation problem and an iterative algorithm are proposed to determine model parameters for multi-sphere SVDD to provide a better data description to the normal data set. We prove that the classification error will be reduced after each iteration in this learning process. Experimental results on 28 well-known data sets show that the proposed multi-sphere SVDD provides lower classification error rate comparing with the standard single-sphere SVDD
Original languageEnglish
Title of host publicationInternational Conference on Neural Information Processing, ICONIP 2010
Subtitle of host publicationNeural Information Processing. Models and Applications
Place of PublicationBerlin, Germany
Number of pages11
ISBN (Print)9783642175336
Publication statusPublished - 2010
EventICONIP 2010 - 17th International Conference on Neural Information Processing - Sydney, Australia
Duration: 22 Nov 201025 Nov 2010


ConferenceICONIP 2010 - 17th International Conference on Neural Information Processing


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