Fast One-Class support Vector Machine for novelty detection

Trung Le, Dinh Phung, Khanh Nguyen, Svetha Venkatesh

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

3 Citations (Scopus)


Novelty detection arises as an important learning task in several applications. Kernel-based approach to novelty detection has been widely used due to its theoretical rigor and elegance of geometric interpretation. However, computational complexity is a major obstacle in this approach. In this paper, leveraging on the cutting-plane framework with the well-known One-Class Support Vector Machine, we present a new solution that can scale up seamlessly with data. The first solution is exact and linear when viewed through the cutting-plane; the second employed a sampling strategy that remarkably has a constant computational complexity defined relatively to the probability of approximation accuracy. Several datasets are benchmarked to demonstrate the credibility of our framework.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining
Subtitle of host publication19th Pacific-Asia Conference, PAKDD 2015 Ho Chi Minh City, Vietnam, May 19–22, 2015 Proceedings, Part II
EditorsTru Cao, Ee-Peng Lim, Zhi-Hua Zhou, Tu-Bao Ho, David Cheung, Hiroshi Motoda
Place of PublicationCham Switzerland
Number of pages12
ISBN (Electronic)9783319180328
ISBN (Print)9783319180311
Publication statusPublished - 2015
Externally publishedYes
EventPacific Asia Conference on Information Systems 2015 - , Singapore
Duration: 5 Jul 20159 Jul 2015
Conference number: 19th

Publication series

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


ConferencePacific Asia Conference on Information Systems 2015
Abbreviated titlePACIS 2015
Internet address


  • Large-scale dataset
  • Novelty detection
  • One-class Support Vector Machine

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