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Learning from Labeled and Unlabeled Data on a Directed Graph


Conference Paper


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

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Event Name: ICML 2005
Event Place: Bonn, Germany

Address: New York, NY, USA
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PostScript


  title = {Learning from Labeled and Unlabeled Data on a Directed Graph},
  author = {Zhou, D. and Huang, J. and Sch{\"o}lkopf, B.},
  booktitle = {Proceedings of the 22nd International Conference on Machine Learning},
  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},
  month_numeric = {8}