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Semi-supervised learning methods are motivated by the availability of largedatasets with unlabeled features in addition to labeled data. Unlabeled datais, however, not guaranteed to improve classification performance and has infact been reported to impair the performance in certain cases. A fundamentalsource of error arises from restrictive assumptions about the unlabeledfeatures, which result in unreliable classifiers that underestimate theirprediction error probabilities. In this paper, we develop a semi-supervisedlearning approach that relaxes such assumptions and is capable of providingclassifiers that reliably quantify the label uncertainty. The approach isapplicable using any generative model with a supervised learning algorithm. Weillustrate the approach using both handwritten digit and cloth classificationdata where the labels are missing at random.

Reliable Semi-Supervised Learning when Labels are Missing at Random