Semi-supervised classification by local coordination

Authors:
Gelan Yang;Xue Xu;Gang Yang;Jianming Zhang
Affiliations:
Hunan City University, Department of Computer Science, Yiyang, China;University of Science and Technology of China, Department of Automation, Hefei, China;Department of Power & Energy Systems, Ecole Supérieur d'Electricité, Supélec, Gif-sur-Yvette Cedex, Franc;College of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, China
Venue:
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Year:
2010

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Abstract

Graph-based methods for semi-supervised learning use graph to smooth the labels of the points. However, most of them are transductive thus can't give predictions for the unlabeled data outside the training set directly. In this paper, we propose an inductive graph-based algorithm that produces a classifier defined on the whole ambient space. A smooth nonlinear projection between the sample space and the label value space is achieved by local dimension reduction and coordination. The effectiveness of the proposed algorithm is demonstrated by the experiment.