Learning from labeled and unlabeled data on a directed graph

  • Authors:
  • Dengyong Zhou;Jiayuan Huang;Bernhard Schölkopf

  • Affiliations:
  • Max Planck Institute for Biological Cybernetics, Tübingen, Germany;University of Waterloo, Waterloo ON, Canada;Max Planck Institute for Biological Cybernetics, Tübingen, Germany

  • Venue:
  • ICML '05 Proceedings of the 22nd international conference on Machine learning
  • Year:
  • 2005

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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.