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Learning from Labeled and Unlabeled Data on a Directed Graph

2005

Talk

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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.
Year: 2005
Month: August
Day: 0

Department(s): Empirical Inference
Bibtex Type: Talk (talk)

Digital: 0
Event Name: The 22nd International Conference on Machine Learning
Event Place: Bonn, Germany
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF

BibTex

@talk{3518,
  title = {Learning from Labeled and Unlabeled Data on a Directed Graph},
  author = {Zhou, D.},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = aug,
  year = {2005},
  month_numeric = {8}
}