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ICML
2005
IEEE

Learning from labeled and unlabeled data on a directed graph

9 years 8 months ago
Learning from labeled and unlabeled data on a directed graph
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.
Bernhard Schölkopf, Dengyong Zhou, Jiayuan Hu
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2005
Where ICML
Authors Bernhard Schölkopf, Dengyong Zhou, Jiayuan Huang
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