The use of support vector machines in semi-supervised classification

Hyunjoo Bae, Hyungwoo Kim, Seung Jun Shin

Research output: Contribution to journalArticlepeer-review


Semi-supervised learning has gained significant attention in recent applications. In this article, we provide a selective overview of popular semi-supervised methods and then propose a simple but effective algorithm for semi-supervised classification using support vector machines (SVM), one of the most popular binary classifiers in a machine learning community. The idea is simple as follows. First, we apply the dimension reduction to the unlabeled observations and cluster them to assign labels on the reduced space. SVM is then employed to the combined set of labeled and unlabeled observations to construct a classification rule. The use of SVM enables us to extend it to the nonlinear counterpart via kernel trick. Our numerical experiments under various scenarios demonstrate that the proposed method is promising in semi-supervised classification.

Original languageEnglish
Pages (from-to)193-202
Number of pages10
JournalCommunications for Statistical Applications and Methods
Issue number2
Publication statusPublished - 2022 Mar


  • Dimension reduction
  • k-means clustering
  • semi-supervised classification
  • support vector machines

ASJC Scopus subject areas

  • Statistics and Probability
  • Modelling and Simulation
  • Finance
  • Statistics, Probability and Uncertainty
  • Applied Mathematics


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