Self-training with adaptive regularization for S3VM

The Semi-Supervised Support Vector Machine (S3VM) solves a non-convex, Mixed-Integer Program (MIP). Due to difficulty in solving the problem, convex approximations have typically been used. However, existing approaches suffer from poor scalability and struggle on certain datasets, compared to graph based counterparts. The poor predictive performance suggests that for some datasets, convex approximations may not be a sufficiently accurate approximation to the problem. We present a self-training approach with self-adapting regularization parameters for S3VM formulations. At each iteration, the regularization parameters are adapted to better reflect label confidence, class proportion, and to gradually include more unlabeled points. We show that updating the S3VM framework iteratively in this fashion, the sequence of SVM subproblems can be solved very efficiently and the solution generated by this sequence yields superior performance compared to leading SSL methods.