Unsupervised multiview learning with partial distribution information

We consider a training data collection mechanism wherein, instead of annotating each training instance with a class label, additional features drawn from a known class-conditional distribution are acquired concurrently. Considering true labels as latent variables, a maximum likelihood approach is proposed to train a classifier based on these unlabeled training data. Furthermore, the case of correlated training instances is considered, wherein latent label variables for subsequently collected training instances form a first-order Markov chain. A convex optimization approach and expectation-maximization algorithms are presented to train classifiers. The efficacy of the proposed approach is validated using the experiments with the iris data and the MNIST handwritten digit data.