Unsupervised Supervised Learning II: Training Margin Based Classifiers without Labels

Details Unsupervised Supervised Learning II: Training Margin Based Classifiers without Labels Unsupervised Supervised Learning II: Training Margin Based Classifiers without Labels

2010-03-01

arxiv.org/abs/1003.0470v2

Computer Science

Learning

22 pages, 43 figures

Scientific paper

Many popular linear classifiers, such as logistic regression, boosting, or SVM, are trained by optimizing a margin-based risk function. 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 the marginal label distribution. 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 with no labeled data whatsoever.

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