Higher order learning with graphs
Research output: Contribution to journal › Conference article › Research › peer-review
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.
Original language | English |
---|---|
Journal | ACM International Conference Proceeding Series |
Pages (from-to) | 17-24 |
Number of pages | 8 |
DOIs | |
Publication status | Published - 2006 |
Externally published | Yes |
Event | 23rd International Conference on Machine Learning, ICML 2006 - Pittsburgh, PA, United States Duration: 25 Jun 2006 → 29 Jun 2006 |
Conference
Conference | 23rd International Conference on Machine Learning, ICML 2006 |
---|---|
Country | United States |
City | Pittsburgh, PA |
Period | 25/06/2006 → 29/06/2006 |
Sponsor | Carnegie Mellon, National Science Foundation, Microsoft, Google, Inc., The Boeing Company |
ID: 302053233