A Continuation Method for Semi-Supervised SVMs
Olivier Chapelle, Mingmin Chi and Alexander Zien
Semi-Supervised Support Vector Machines (S3VMs) are an appealing
method for using unlabeled data in classification: their objective
function favors decision boundaries which do not cut clusters. However
their main problem is that the optimization problem is non-convex and
has many local minima, which often results in suboptimal
performances. In this paper we propose to use a global optimization
technique known as continuation to alleviate this problem.
Compared to other algorithms minimizing the same objective function,
our continuation method often leads to lower test errors.