Optimization Techniques for Semi-Supervised Support Vector Machines

Abstract

Due to its wide applicability, the problem of semi-supervised classification is attracting increasing attention in machine learning. Semi-Supervised Support Vector Machines (S 3 VMs) are based on applying the margin maximization principle to both labeled and unlabeled examples. Unlike SVMs, their formulation leads to a non-convex optimization problem. A suite of algorithms have recently been proposed for solving S 3 VMs. This paper reviews key ideas in this literature. The performance and behavior of various S 3 VMs algorithms is studied together, under a common experimental setting.