Optimization Approaches to Semi-Supervised Learning
We examine mathematical models for semi-supervised support vector machines (S3VM). Given a training set of labeled data and a working set of unlabeled data, S3VM constructs a support vector machine using both the training and working sets. We use S3VM to solve the transductive inference problem posed by Vapnik. In transduction, the task is to estimate the value of a classification function at the given points in the working set. This contrasts with inductive inference which estimates the classification function at all possible values. We propose a general S3VM model that minimizes both the misclassification error and the function capacity based on all the available data. Depending on how poorly-estimated unlabeled data are penalized, different mathematical models result. We examine several practical algorithms for solving these model. The first approach utilizes the S3VM model for 1-norm linear support vector machines converted to a mixed-integer program (MIP). A global solution of the MIP is found using a commercial integer programming solver. The second approach uses a nonconvex quadratic program. Variations of block-coordinate-descent algorithms are used to find local solutions of this problem. Using this MIP within a local learning algorithm produced the best results. Our experimental study on these statistical learning methods indicates that incorporating working data can improve generalization.