On efficient large margin semisupervised learning: Method and theory

Junhui Wang, Xiaotong Shen, Wei Pan

Research output: Contribution to journalArticlepeer-review

38 Scopus citations


In classification, semisupervised learning usually involves a large amount of unlabeled data with only a small number of labeled data. This imposes a great challenge in that it is difficult to achieve good classification performance through labeled data alone. To leverage unlabeled data for enhancing classification, this article introduces a large margin semisupervised learning method within the framework of regularization, based on an efficient margin loss for unlabeled data, which seeks efficient extraction of the information from unlabeled data for estimating the Bayes decision boundary for classification. For implementation, an iterative scheme is derived through conditional expectations. Finally, theoretical and numerical analyses are conducted, in addition to an application to gene function prediction. They suggest that the proposed method enables to recover the performance of its supervised counterpart based on complete data in rates of convergence, when possible.

Original languageEnglish (US)
Pages (from-to)719-742
Number of pages24
JournalJournal of Machine Learning Research
StatePublished - Jan 2009


  • Classification
  • Difference convex programming
  • Nonconvex minimization
  • Regulanzation
  • Support vectors


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