关键词: 半监督学习, 分类, 流形正则化, 成对约束

Abstract: In order to improve the learning performance, semi-supervised learning methods aim at exploiting the knowledge of a small amount of labeled examples as well as lots of unlabeled data instances simultaneously. However, most existing semi-supervised approaches, primarily focus on the effective utilization of those label-unknown data, and the successive study regarding the label-known examples is usually neglected. In light of such situation, in terms of the manifold regularization framework, this paper proposes a novel semi-supervised classification method based on joint regularization of manifold and pairwise constraints (SSC-JRMPC). This method proceeds from two aspects: on one hand, inheriting from the manifold regularization framework, the optimization regarding both empirical risk and structural risk, and the use of intrinsic data distribution of entire dataset are considered concurrently; on the other hand, by transforming the sample labels into the must-link/cannot-link pairwise constraint conditions and incorporating these extended knowledge into own objective formulation, the knowledge existing in the supervision information is further mined. As the results, the classification accuracy of SSC-JRMPC is distinctly enhanced. The experiments on real-world datasets confirm the merits of this paper work.

Key words: semi-supervised learning, classification, manifold regularization, pairwise constraints