Learning from Labeled and Unlabeled Data on a Directed Graph
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.
Cite
Text
Zhou et al. "Learning from Labeled and Unlabeled Data on a Directed Graph." International Conference on Machine Learning, 2005. doi:10.1145/1102351.1102482Markdown
[Zhou et al. "Learning from Labeled and Unlabeled Data on a Directed Graph." International Conference on Machine Learning, 2005.](https://mlanthology.org/icml/2005/zhou2005icml-learning/) doi:10.1145/1102351.1102482BibTeX
@inproceedings{zhou2005icml-learning,
title = {{Learning from Labeled and Unlabeled Data on a Directed Graph}},
author = {Zhou, Dengyong and Huang, Jiayuan and Schölkopf, Bernhard},
booktitle = {International Conference on Machine Learning},
year = {2005},
pages = {1036-1043},
doi = {10.1145/1102351.1102482},
url = {https://mlanthology.org/icml/2005/zhou2005icml-learning/}
}