Synthesis of Maximum Margin and Multiview Learning using Unlabeled Data
In this presentation we show the semi-supervised learning with two input sources can be transformed into a maximum margin problem to be similar to a binary SVM. 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.