Kernel-based semi-supervised learning for novelty detection

Van Nguyen, Trung Le, Thien Pham, Mi Dinh, Thai Hoang Le

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

5 Citations (Scopus)


One-class Support Vector Machine (OCSVM) is a well-known method for novelty detection. However, OCSVM regards all negative data samples as a common symbol and thereby not being able to utilize the information carried by them. Furthermore, OCSVM requires a fully labeled data set and cannot work efficiently with data set with both labeled and unlabeled data samples which is very popular nowadays. In this paper, we first extend the model of OCSVM to enable efficiently using the negative data samples. We then propose two methods to integrate the semi-supervised learning paradigm to the extended model for novelty detection purpose.

Original languageEnglish
Title of host publicationProceedings of the 2014 International Joint Conference on Neural Networks
EditorsAlessandro Sperduti, Cesare Alippi
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages8
ISBN (Electronic)9781479914845, 9781479966271
ISBN (Print)9781479914821
Publication statusPublished - 2014
Externally publishedYes
EventIEEE International Joint Conference on Neural Networks 2014 - Beijing, China
Duration: 6 Jul 201411 Jul 2014 (Proceedings)


ConferenceIEEE International Joint Conference on Neural Networks 2014
Abbreviated titleIJCNN 2014
Internet address


  • Kernel Method
  • Novelty Detection
  • One-class Classification
  • Semi-supervised Learning

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