Semi-supervised learning by spectral mapping with label information

Authors:
Zhong-Qiu Zhao;Jun Gao;Xindong Wu
Affiliations:
School of Computer & Information, Hefei University of Technology, Hefei, Anhui, China;School of Computer & Information, Hefei University of Technology, Hefei, Anhui, China;Department of Computer Science, University of Vermont
Venue:
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part I
Year:
2010

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Abstract

A novel version of spectral mapping for partially labeled sample classification is proposed in this paper. This new method adds the label information into the mapping process, and adopts the geodesic distance rather than Euclidean distance as the measure of the difference between two data points. The experimental results show that the proposed method yields significant benefits for partially labeled classification with respect to the previous methods.