Semi-supervised learning based on high density region estimation.

In this paper, we consider local regression problems on high density regions. We propose a semi-supervised local empirical risk minimization algorithm and bound its generalization error. The theoretical analysis shows that our method can utilize unlabeled data effectively and achieve fast learning rate.