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2008

On Discriminative Semi-Supervised Classification

7 years 8 months ago
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.
Fei Wang, Changshui Zhang
Added 02 Oct 2010
Updated 02 Oct 2010
Type Conference
Year 2008
Where AAAI
Authors Fei Wang, Changshui Zhang
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