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.
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Yang, G., Xu, X., Yang, G., Zhang, J. (2010). Semi-supervised Classification by Local Coordination. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Models and Applications. ICONIP 2010. Lecture Notes in Computer Science, vol 6444. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17534-3_64
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DOI: https://doi.org/10.1007/978-3-642-17534-3_64
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