A theoretical framework for multi-sphere support vector data description

Trung Le, Dat Tran, Wanli Ma, Dharmendra Sharma

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-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 publicationNeural Information Processing. Models and Applications
Subtitle of host publication17th International Conference, ICONIP 2010, Sydney, Australia, November 22-25, 2010, Proceedings, Part II
EditorsKok Wai Wong, B. Sumudu, U. Mendis, Abdesselam Bouzerdoum
Place of PublicationBerlin Germany
Number of pages11
ISBN (Print)3642175333, 9783642175336
Publication statusPublished - 2010
Externally publishedYes
EventInternational Conference on Neural Information Processing 2010 - Sydney, Australia
Duration: 22 Nov 201025 Nov 2010
Conference number: 17th
https://link.springer.com/book/10.1007/978-3-642-17537-4 (Proceedings)

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Conference on Neural Information Processing 2010
Abbreviated titleICONIP 2010
Internet address


  • imbalanced data
  • novelty detection
  • one-class classification
  • spherically shaped boundary
  • Support vector data description

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