  Semi-supervised learning methods are motivated by the availability of large
datasets with unlabeled features in addition to labeled data. Unlabeled data
is, however, not guaranteed to improve classification performance and has in
fact been reported to impair the performance in certain cases. A fundamental
source of error arises from restrictive assumptions about the unlabeled
features, which result in unreliable classifiers that underestimate their
prediction error probabilities. In this paper, we develop a semi-supervised
learning approach that relaxes such assumptions and is capable of providing
classifiers that reliably quantify the label uncertainty. The approach is
applicable using any generative model with a supervised learning algorithm. We
illustrate the approach using both handwritten digit and cloth classification
data where the labels are missing at random.
