Abstract
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.
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Zhao, L., Luo, S., Tian, M., Shao, C., Ma, H. (2006). Combining Label Information and Neighborhood Graph for Semi-supervised Learning. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3971. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11759966_72
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DOI: https://doi.org/10.1007/11759966_72
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34439-1
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