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Erschienen in:
2006 | OriginalPaper | Buchkapitel
Combining Label Information and Neighborhood Graph for Semi-supervised Learning
verfasst von : Lianwei Zhao, Siwei Luo, Mei Tian, Chao Shao, Hongliang Ma
Erschienen in: Advances in Neural Networks - ISNN 2006
Verlag: Springer Berlin Heidelberg
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In this paper, we consider the problem of combining the labeled and unlabeled examples to boost the performance of semi-supervised learning. We first define the label information graph, and then incorporate it with neighborhood graph. We propose a new regularized semi-supervised classification algorithm, in which the regularization term is based on this modified Graph Laplacian. According to the properties of Reproducing Kernel Hilbert Space (RKHS), the representer theorem holds, so the solution can be expressed by the Mercer kernel of examples. Experimental results show that our algorithm can use unlabeled and labeled examples effectively.