Semi-supervised learning based on the low-density separation principle such as the cluster and manifold assumptions has been extensively studied in the last decades. However, such semi-supervised learning methods do not always perform well due to violation of the cluster and manifold assumptions. In this paper, we propose a novel approach to semi-supervised