Graph-based semi-supervised Support Vector Data Description for novelty detection

Phuong Duong, Van Nguyen, Mi Dinh, Trung Le, Dat Tran, Wanli Ma

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

7 Citations (Scopus)


Support Vector Data Description (SVDD) is a well-known supervised learning method for novelty detection purpose. For its classification task, SVDD requires a fully-labeled dataset. Nonetheless, contemporary datasets always consist of a collection of labeled data samples jointly a much larger collection of unlabeled ones. This fact impedes the usage of SVDD in the real-world problems. In this paper, we propose to utilize the information implicated in a spectral graph to leverage SVDD in the context of semi-supervised learning. The theory and experiment evidence that the proposed method is able to efficiently employ the information carried in the spectral graph to not only enhance the generalization ability of SVDD but also enforce the cluster assumption which is crucial for a semi-supervised learning method.

Original languageEnglish
Title of host publication2015 International Joint Conference on Neural Networks (IJCNN 2015)
EditorsHaibo He, Asim Roy
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9781479919604, 9781479919598
ISBN (Print)9781479919611
Publication statusPublished - 2015
Externally publishedYes
EventIEEE International Joint Conference on Neural Networks 2015 - Killarney Ireland, Killarney, Ireland
Duration: 12 Jul 201517 Jul 2015 (Proceedings)


ConferenceIEEE International Joint Conference on Neural Networks 2015
Abbreviated titleIJCNN 2015
Internet address


  • Kernel method
  • novelty detection
  • one-class classification
  • semi-supervised learning
  • subspace learning

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