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.
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Zhao, ZQ., Gao, J., Wu, X. (2010). Semi-supervised Learning by Spectral Mapping with Label Information. In: Wang, F.L., Deng, H., Gao, Y., Lei, J. (eds) Artificial Intelligence and Computational Intelligence. AICI 2010. Lecture Notes in Computer Science(), vol 6319. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16530-6_53
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DOI: https://doi.org/10.1007/978-3-642-16530-6_53
Publisher Name: Springer, Berlin, Heidelberg
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