Unsupervised Supervised Learning II: Margin-Based Classification without Labels

Krishnakumar Balasubramanian, Pinar Donmez, Guy Lebanon; JMLR W&CP 15:137-145, 2011.

Abstract

Many popular linear classifiers, such as logistic regression, boosting, or SVM, are trained by optimizing margin-based risk functions. Traditionally, these risk functions are computed based on a labeled dataset. We develop a novel technique for estimating such risks using only unlabeled data and knowledge of p(y). We prove that the proposed risk estimator is consistent on high-dimensional datasets and demonstrate it on synthetic and real-world data. In particular, we show how the estimate is used for evaluating classifiers in transfer learning, and for training classifiers using exclusively unlabeled data.

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