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Abstract

An intuitive approach to utilizing unlabeled data in kernel-based
classification algorithms is to simply treat the unknown labels as
additional optimization variables. For margin-based loss functions,
one can view this approach as attempting to learn low-density
separators. However, this is a hard optimization problem to solve in
typical semi-supervised settings where unlabeled data is abundant.
The popular Transductive SVM algorithm is a
label-switching-retraining procedure that is known to be susceptible
to local minima. In this paper, we present a global optimization
framework for semi-supervised Kernel machines where an easier
problem is parametrically deformed to the original hard problem and
minimizers are smoothly tracked. Our approach is motivated from
deterministic annealing techniques and involves a sequence of convex
optimization problems that are exactly and efficiently solved. We
present empirical results on several synthetic and real world
datasets that demonstrate the effectiveness of our approach.