Higher order learning with graphs

Research output: Contribution to journal › Conference article › Research › peer-review

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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

Harvard

Agarwal, S, Branson, K & Belongie, S 2006, 'Higher order learning with graphs', ACM International Conference Proceeding Series, pp. 17-24. https://doi.org/10.1145/1143844.1143847

APA

Agarwal, S., Branson, K., & Belongie, S. (2006). Higher order learning with graphs. ACM International Conference Proceeding Series, 17-24. https://doi.org/10.1145/1143844.1143847

Vancouver

Agarwal S, Branson K, Belongie S. Higher order learning with graphs. ACM International Conference Proceeding Series. 2006;17-24. https://doi.org/10.1145/1143844.1143847

Author

Agarwal, Sameer ; Branson, Kristin ; Belongie, Serge. / Higher order learning with graphs. In: ACM International Conference Proceeding Series. 2006 ; pp. 17-24.

Bibtex

@inproceedings{5b525137a300489991ab35cbd4ddb1d7,
title = "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.",
author = "Sameer Agarwal and Kristin Branson and Serge Belongie",
year = "2006",
doi = "10.1145/1143844.1143847",
language = "English",
pages = "17--24",
journal = "ACM International Conference Proceeding Series",
note = "23rd International Conference on Machine Learning, ICML 2006 ; Conference date: 25-06-2006 Through 29-06-2006",

}

RIS

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