Higher order learning with graphs
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Higher order learning with graphs. / Agarwal, Sameer; Branson, Kristin; Belongie, Serge.
In: ACM International Conference Proceeding Series, 2006, p. 17-24.Research output: Contribution to journal › Conference article › Research › peer-review
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TY - GEN
T1 - Higher order learning with graphs
AU - Agarwal, Sameer
AU - Branson, Kristin
AU - Belongie, Serge
PY - 2006
Y1 - 2006
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=34250754984&partnerID=8YFLogxK
U2 - 10.1145/1143844.1143847
DO - 10.1145/1143844.1143847
M3 - Conference article
AN - SCOPUS:34250754984
SP - 17
EP - 24
JO - ACM International Conference Proceeding Series
JF - ACM International Conference Proceeding Series
T2 - 23rd International Conference on Machine Learning, ICML 2006
Y2 - 25 June 2006 through 29 June 2006
ER -
ID: 302053233