Multilabel Classification using Bayesian Compressed Sensing

In this paper, we present a Bayesian framework for multilabel classification using compressed sensing. The key idea in compressed sensing for multilabel classification is to first project the label vector to a lower dimensional space using a random transformation and then learn regression functions over these projections. Our approach considers both of these components in a single probabilistic model, thereby jointly optimizing over compression as well as learning tasks. We then derive an efficient variational inference scheme that provides joint posterior distribution over all the unobserved labels. The two key benefits of the model are that a) it can naturally handle datasets that have missing labels and b) it can also measure uncertainty in prediction.