Synthesis of maximum margin and multiview learning using unlabeled data ?
In this paper we show that the semi-supervised learning with two input sources can be transformed into a maximum margin problem to be similar to a binary support vector machine. Our formulation exploits the unlabeled data to reduce the complexity of the class of the learning functions. In order to measure how the complexity is decreased we use the Rademacher complexity theory. The corresponding optimization problem is convex and it is efficiently solvable for large-scale applications as well.
maximum margin multiview learning semi-supervised learning support vector machine rademacher complexity