Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/405542
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dc.contributorAgarwal, Sameeren_US
dc.contributorLim, Jongwooen_US
dc.contributorZelnik-Manor, Lihien_US
dc.contributorPerona, Pietroen_US
dc.contributorKriegman, Daviden_US
dc.contributorBelongie, Sergeen_US
dc.date2005en_US
dc.date.accessioned2020-05-18T11:45:24Z-
dc.date.available2020-05-18T11:45:24Z-
dc.date.issued2008en_US
dc.identifier.other10.1109/CVPR.2005.89en_US
dc.identifier.urihttp://localhost/handle/Hannan/375954en_US
dc.identifier.urihttp://localhost/handle/Hannan/405542-
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstractWe consider the problem of clustering in domains where the affinity relations are not dyadic (pairwise), but rather triadic, tetradic or higher. The problem is an instance of the hypergraph partitioning problem. We propose a two-step algorithm for solving this problem. In the first step we use a novel scheme to approximate the hypergraph using a weighted graph. In the second step a spectral partitioning algorithm is used to partition the vertices of this graph. The algorithm is capable of handling hyperedges of all orders including order two, thus incorporating information of all orders simultaneously. We present a theoretical analysis that relates our algorithm to an existing hypergraph partitioning algorithm and explain the reasons for its superior performance. We report the performance of our algorithm on a variety of computer vision problems and compare it to several existing hypergraph partitioning algorithms.en_US
dc.relation.haspartAL507044.pdfen_US
dc.subjectScience & Technologyen_US
dc.titleBeyond pairwise clusteringen_US
dc.typeArticleen_US
dc.journal.titleProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognitionen_US
Appears in Collections:2002-2008

Files in This Item:
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AL507044.pdf178.77 kBAdobe PDF
Full metadata record
DC FieldValueLanguage
dc.contributorAgarwal, Sameeren_US
dc.contributorLim, Jongwooen_US
dc.contributorZelnik-Manor, Lihien_US
dc.contributorPerona, Pietroen_US
dc.contributorKriegman, Daviden_US
dc.contributorBelongie, Sergeen_US
dc.date2005en_US
dc.date.accessioned2020-05-18T11:45:24Z-
dc.date.available2020-05-18T11:45:24Z-
dc.date.issued2008en_US
dc.identifier.other10.1109/CVPR.2005.89en_US
dc.identifier.urihttp://localhost/handle/Hannan/375954en_US
dc.identifier.urihttp://localhost/handle/Hannan/405542-
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstractWe consider the problem of clustering in domains where the affinity relations are not dyadic (pairwise), but rather triadic, tetradic or higher. The problem is an instance of the hypergraph partitioning problem. We propose a two-step algorithm for solving this problem. In the first step we use a novel scheme to approximate the hypergraph using a weighted graph. In the second step a spectral partitioning algorithm is used to partition the vertices of this graph. The algorithm is capable of handling hyperedges of all orders including order two, thus incorporating information of all orders simultaneously. We present a theoretical analysis that relates our algorithm to an existing hypergraph partitioning algorithm and explain the reasons for its superior performance. We report the performance of our algorithm on a variety of computer vision problems and compare it to several existing hypergraph partitioning algorithms.en_US
dc.relation.haspartAL507044.pdfen_US
dc.subjectScience & Technologyen_US
dc.titleBeyond pairwise clusteringen_US
dc.typeArticleen_US
dc.journal.titleProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognitionen_US
Appears in Collections:2002-2008

Files in This Item:
File SizeFormat 
AL507044.pdf178.77 kBAdobe PDF
Full metadata record
DC FieldValueLanguage
dc.contributorAgarwal, Sameeren_US
dc.contributorLim, Jongwooen_US
dc.contributorZelnik-Manor, Lihien_US
dc.contributorPerona, Pietroen_US
dc.contributorKriegman, Daviden_US
dc.contributorBelongie, Sergeen_US
dc.date2005en_US
dc.date.accessioned2020-05-18T11:45:24Z-
dc.date.available2020-05-18T11:45:24Z-
dc.date.issued2008en_US
dc.identifier.other10.1109/CVPR.2005.89en_US
dc.identifier.urihttp://localhost/handle/Hannan/375954en_US
dc.identifier.urihttp://localhost/handle/Hannan/405542-
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstractWe consider the problem of clustering in domains where the affinity relations are not dyadic (pairwise), but rather triadic, tetradic or higher. The problem is an instance of the hypergraph partitioning problem. We propose a two-step algorithm for solving this problem. In the first step we use a novel scheme to approximate the hypergraph using a weighted graph. In the second step a spectral partitioning algorithm is used to partition the vertices of this graph. The algorithm is capable of handling hyperedges of all orders including order two, thus incorporating information of all orders simultaneously. We present a theoretical analysis that relates our algorithm to an existing hypergraph partitioning algorithm and explain the reasons for its superior performance. We report the performance of our algorithm on a variety of computer vision problems and compare it to several existing hypergraph partitioning algorithms.en_US
dc.relation.haspartAL507044.pdfen_US
dc.subjectScience & Technologyen_US
dc.titleBeyond pairwise clusteringen_US
dc.typeArticleen_US
dc.journal.titleProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognitionen_US
Appears in Collections:2002-2008

Files in This Item:
File SizeFormat 
AL507044.pdf178.77 kBAdobe PDF