On Discriminative Semi-Supervised Classification

The recent years have witnessed a surge of interests in semi-supervised learning methods. A common strategy for these algorithms is to require that the predicted data labels should be sufficiently smooth with respect to the intrinsic data manifold. In this paper, we argue that rather than penalizing the label smoothness, we can directly punish the discriminality of the classification function to achieve a more powerful predictor, and we derive two specific algorithms: SemiSupervised Discriminative Regularization (SSDR) and Semi-parametric Discriminative Semi-supervised Classification (SDSC). Finally many experimental results are presented to show the effectiveness of our method.

• Type: Conference
• Year: 2008
• Where: AAAI
• Authors: Fei Wang, Changshui Zhang
• Keywords: AAAI 2008, Discriminative Regularization, Discriminative Semi-supervised Classification, Intelligent Agents, Semi-supervised Learning Methods