Infinitesimal Annealing for Training Semi-Supervised Support Vector Machines

The semi-supervised support vector machine (SVM) is a maximum-margin classification algorithm based on both labeled and unlabeled data. Training SVM involves either a combinatorial or non-convex optimization problem and thus finding the global optimal solution is intractable in practice. It has been demonstrated that a key to successfully find a good (local) solution of SVM is to gradually increase the effect of unlabeled data, à la annealing. However, existing algorithms suffer from the trade-off between the resolution of annealing steps and the computation cost. In this paper, we go beyond this trade-off by proposing a novel training algorithm that efficiently performs annealing with an infinitesimal resolution. Through experiments, we demonstrate that the proposed infinitesimal annealing algorithm tends to produce better solutions with less computation time than existing approaches.