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Conference Paper

Learning from Labeled and Unlabeled Data on a Directed Graph

MPS-Authors
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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;

/persons/resource/persons84193

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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LPDG_3463.pdf
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Citation

Zhou, D., Huang, J., & Schölkopf, B. (2005). Learning from Labeled and Unlabeled Data on a Directed Graph. In S. Jozef Stefan Institute, Slovenia Program Chairs: Luc D, L. de Raedt, & S. Wrobel (Eds.), ICML '05: 22nd international conference on Machine learning (pp. 1036-1043). New York, NY, USA: ACM Press.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-D4BB-6
Abstract
We propose a general framework for learning from labeled and unlabeled data on a directed graph in which the structure of the graph including the directionality of the edges is considered. The time complexity of the algorithm derived from this framework is nearly linear due to recently developed numerical techniques. In the absence of labeled instances, this framework can be utilized as a spectral clustering method for directed graphs, which generalizes the spectral clustering approach for undirected graphs. We have applied our framework to real-world web classification problems and obtained encouraging results.