Self-training classifier via local learning regularization

Self-training learning is one of the most important semi-supervised learning paradigms in which a learner keeps on classifying the unlabeled examples and retaining the most confident examples to the training set. With the increasing training set, it is possible to enhance the classification performance on unseen data. However, sometimes the classifier misclassifies some unlabeled examples and keeps them in the training set, which worse the classification performance. In this paper, we present a novel method based on local consistency to eliminate the noises. According the manifold assumption, an unlabeled example expects to join the training set if its label given by classifier should be consistent with the local neighborhood in the training set on the manifold. We test the new method on several data sets from synthetic and real-world data from UCI, the empirical result indicates the proposed approach is effective and reliable.