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  Beyond Pairwise Classification and Clustering Using Hypergraphs

Zhou, D., Huang, J., & Schölkopf, B.(2005). Beyond Pairwise Classification and Clustering Using Hypergraphs (143). Tübingen, Germany: Max Planck Institute for Biological Cybernetics.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-D49D-8 Version Permalink: https://hdl.handle.net/21.11116/0000-0002-8810-2
Genre: Report

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MPIK-TR-143.pdf (Publisher version), 251KB
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 Creators:
Zhou, D1, 2, Author           
Huang, J1, 2, Author           
Schölkopf, B1, 2, Author           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: In many applications, relationships among objects of interest are more complex than pairwise. Simply approximating complex relationships as pairwise ones can lead to loss of information. An alternative for these applications is to analyze complex relationships among data directly, without the need to first represent the complex relationships into pairwise ones. A natural way to describe complex relationships is to use hypergraphs. A
hypergraph is a graph in which edges can connect more than two vertices. Thus we consider learning from a hypergraph, and develop a general framework which is applicable to classification and clustering for complex relational data. We have applied our framework to real-world web classification problems and obtained encouraging results.

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 Dates: 2005-08
 Publication Status: Published in print
 Pages: 13
 Publishing info: Tübingen, Germany : Max Planck Institute for Biological Cybernetics
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 Rev. Type: -
 Identifiers: Report Nr.: 143
BibTex Citekey: 3529
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Title: Technical Report of the Max Planck Institute for Biological Cybernetics
Source Genre: Series
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Pages: - Volume / Issue: 143 Sequence Number: - Start / End Page: - Identifier: -