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Beyond Pairwise Classification and Clustering Using Hypergraphs


Technical Report


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.

Author(s): Zhou, D. and Huang, J. and Schölkopf, B.
Number (issue): 143
Year: 2005
Month: August
Day: 18

Department(s): Empirical Inference
Bibtex Type: Technical Report (techreport)

Institution: Max Planck Institute for Biological Cybernetics

Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF


  title = {Beyond Pairwise Classification and Clustering Using Hypergraphs},
  author = {Zhou, D. and Huang, J. and Sch{\"o}lkopf, B.},
  number = {143},
  organization = {Max-Planck-Gesellschaft},
  institution = {Max Planck Institute for Biological Cybernetics},
  school = {Biologische Kybernetik},
  month = aug,
  year = {2005},
  month_numeric = {8}