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Efficient large margin semisupervised learning

Junhui Wang; JMLR W&P 2:588-595, 2007.

Abstract

In classification, semisupervised learning involves a large amount of unlabeled data with only a small number of labeled data. This imposes great challenge in that the class probability given input can not be well estimated through labeled data alone. To enhance predictability of classification, this article introduces a large margin semisupervised learning method constructing an efficient loss to measure the contribution of unlabeled instances to classification. The loss is iteratively refined, based on which an iterative scheme is derived for implementation. The proposed method is examined for two large margin classifiers: support vector machines and $\psi$-learning. Our theoretical and numerical analyses indicate that the method achieves the desired objective of delivering higher performances over any other method initializing the scheme.



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