Higher Order Learning with Graphs
Abstract
Recently there has been considerable interest in learning with higher order relations (i.e., three-way or higher) in the unsupervised and semi-supervised settings. Hypergraphs and tensors have been proposed as the natural way of representing these relations and their corresponding algebra as the natural tools for operating on them. In this paper we argue that hypergraphs are not a natural representation for higher order relations, indeed pairwise as well as higher order relations can be handled using graphs. We show that various formulations of the semi-supervised and the unsupervised learning problem on hypergraphs result in the same graph theoretic problem and can be analyzed using existing tools.
Cite
Text
Agarwal et al. "Higher Order Learning with Graphs." International Conference on Machine Learning, 2006. doi:10.1145/1143844.1143847Markdown
[Agarwal et al. "Higher Order Learning with Graphs." International Conference on Machine Learning, 2006.](https://mlanthology.org/icml/2006/agarwal2006icml-higher/) doi:10.1145/1143844.1143847BibTeX
@inproceedings{agarwal2006icml-higher,
title = {{Higher Order Learning with Graphs}},
author = {Agarwal, Sameer and Branson, Kristin and Belongie, Serge J.},
booktitle = {International Conference on Machine Learning},
year = {2006},
pages = {17-24},
doi = {10.1145/1143844.1143847},
url = {https://mlanthology.org/icml/2006/agarwal2006icml-higher/}
}