title: On Efficient Large Margin Semisupervised Learning: Method and Theory.
abstract: 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.
authors.affiliation: Department of Statistics, Columbia University, New York, NY 10027, USA.
authors.forename: Junhui
authors.initials: J
authors.lastname: Wang
authors.affiliation: School of Statistics, University of Minnesota, Minneapolis, MN 55455, USA.
authors.forename: Xiaotong
authors.initials: X
authors.lastname: Shen
authors.affiliation: Division of Biostatistics, University of Minnesota, Minneapolis, MN 55455, USA.
authors.forename: Wei
authors.initials: W
authors.lastname: Pan
keywords: classification; difference convex programming; nonconvex minimization; regularization; support vectors