Transductive Inference with Graphs

Zhou, D; Schölkopf, B

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Transductive Inference with Graphs

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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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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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Citation

Zhou, D., & Schölkopf, B.(2004). Transductive Inference with Graphs. Tübingen, Germany: Max Planck Institute for Biological Cybernetics.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-F345-1

Abstract

We propose a general regularization framework for transductive
inference. The given data are thought of as a graph, where the
edges encode the pairwise relationships among data. We develop
discrete analysis and geometry on graphs, and then naturally adapt
the classical regularization in the continuous case to the graph
situation. A new and effective algorithm is derived from this
general framework, as well as an approach we developed before.