A simple hybrid method for semi-supervised learning

Abstract

We introduce and describe the Hybrid Semi-Supervised Method (HSSM) for learning. This is the first hybrid method aimed to solve problems with both labeled and unlabeled data. The new method uses an unsupervised stage in order to decompose the full problem into a set of simpler subproblems. HSSM applies simple stopping criteria during the unsupervised stage, which allows the method to concentrate on the difficult portions of the original problem. The new algorithm also makes use of a simple strategy to select at each subproblem a small subset of unlabeled samples that are relevant to modify the decision surface. To this end, HSSM trains a linear SVM on the available labeled samples, and selects the unlabeled samples that lie within the margin of the trained SVM. We evaluated the new method using a previously introduced setup, which includes datasets with very different properties. Overall, the error levels produced by the new HSSM are similar to other SSL methods, but HSSM is shown to be more efficient than all previous methods, using only a small fraction of the available unlabeled data. © 2012 Springer-Verlag.