Data Visualization White Papers
Learning From Labeled and Unlabeled Data on a Directed Graph
Overview This paper proposes 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 in-stances, this framework can be utilized as a spectral clustering method for directed graphs, which generalizes the spectral clustering approach for undirected graphs. This paper applies the framework to real-world web classification problems and obtained encouraging results.
| Publisher | University of Waterloo | File Format | |
|---|---|---|---|
| Date Published | October 2005 | ||
| Format | White Papers | ||
| Topics | |||
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