Semi-supervised classification learning by discrimination-aware manifold regularization

Manifold regularization (MR) provides a powerful framework for semi-supervised classification (SSC) using both the labeled and unlabeled data. It first constructs a single Laplacian graph over the whole dataset for representing the manifold structure, and then enforces the smoothness constraint over such graph by a Laplacian regularizer in learning. However, the smoothness over such a single Laplacian graph may take the risk of ignoring the discrimination among boundary instances, which are very likely from different classes though highly close to each other on the manifold. To compensate for such deficiency, researches have already been devoted by taking into account the discrimination together with the smoothness in learning. However, those works are only confined to the discrimination of the labeled instances, thus rather limited in boosting the semi-supervised learning. To mitigate such an unfavorable situation, we attempt to discover the possible discrimination in the available instances first by performing some unsupervised clustering over the whole dataset, and then incorporate it into MR to develop a novel discrimination-aware manifold regularization (DAMR) framework. In DAMR, instances with high similarity on the manifold will be restricted to share the same class label if belonging to the same cluster, or to have different class labels, otherwise. Our empirical results show the competitiveness of DAMR compared to MR and its variants likewise incorporating the discrimination in learning.