Learning with Local and Global Consistency

Zhou, D; Bousquet, O; Lal, TN; Weston, J; Schölkopf, B

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Learning with Local and Global Consistency

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Zhou, D
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Bousquet, O
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Lal, TN
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Weston, J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Schölkopf, B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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https://papers.nips.cc/paper/2506-learning-with-local-and-global-consistency.pdf
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引用

Zhou, D., Bousquet, O., Lal, T., Weston, J., & Schölkopf, B. (2004). Learning with Local and Global Consistency. In S., Thrun, L., Saul, & B., Schölkopf (Eds.), Advances in Neural Information Processing Systems 16: Proceedings of the 2003 Conference (pp. 321-328). Cambridge, MA, USA: MIT Press.

引用: https://hdl.handle.net/11858/00-001M-0000-0013-D8EF-C

要旨

We consider the general problem of learning from labeled and unlabeled data, which is often called semi-supervised learning or transductive inference. A principled approach to semi-supervised learning is to design a classifying function which is sufficiently smooth with respect to the intrinsic structure collectively revealed by known labeled and unlabeled points. We present a simple algorithm to obtain such a smooth solution. Our method yields encouraging experimental results on a number of classification problems and demonstrates effective use of unlabeled data.