CUPID: consistent unlabeled probability of identical distribution for image classification

We consider the general problem of learning from both labeled and unlabeled data, which is often called semi-supervised learning (SSL) or transductive inference. A principled approach to SSL is to design a classification function that is sufficiently smooth with respect to the underlying structure collectively revealed by known labeled and unlabeled data. Combining transductive learning and inductive learning together, we present a simple and scalable algorithm to obtain such a smooth function, namely, Consistent Unlabeled Probability of Identical Distribution (CUPID). The labels of unlabeled data are taken as the probability, consistent to their identical distribution based on geometric structure of the unlabeled. The proposed algorithm yields encouraging experimental results on a number of image classification problems and demonstrates effective use of unlabeled data.