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Empirical Inference Conference Paper 2005

Learning from Labeled and Unlabeled Data on a Directed Graph

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Empirical Inference
Dengyong Zhou
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Empirical Inference
Bernhard Schölkopf
  • Director
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Empirical Inference
Jiayuan Huang
  • Doctoral Researcher

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.

Author(s): Zhou, D. and Huang, J. and Schölkopf, B.
Book Title: Proceedings of the 22nd International Conference on Machine Learning
Pages: 1041 -1048
Year: 2005
Month: August
Day: 0
Editors: L De Raedt and S Wrobel
Publisher: ACM
Bibtex Type: Conference Paper (inproceedings)
Address: New York, NY, USA
Event Name: ICML 2005
Event Place: Bonn, Germany
Electronic Archiving: grant_archive
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik
Links:

BibTex 

@inproceedings{3463,
  title = {Learning from Labeled and Unlabeled Data on a Directed Graph},
  booktitle = {Proceedings of the 22nd International Conference on Machine Learning},
  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.},
  pages = {1041 -1048},
  editors = {L De Raedt and S Wrobel},
  publisher = {ACM},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {New York, NY, USA},
  month = aug,
  year = {2005},
  slug = {3463},
  author = {Zhou, D. and Huang, J. and Sch{\"o}lkopf, B.},
  month_numeric = {8}
}