Beyond pairwise clustering
Research output: Contribution to journal › Conference article › Research › peer-review
We 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.
Original language | English |
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Journal | Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005 |
Pages (from-to) | 838-845 |
Number of pages | 8 |
DOIs | |
Publication status | Published - 2005 |
Externally published | Yes |
Event | 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005 - San Diego, CA, United States Duration: 20 Jun 2005 → 25 Jun 2005 |
Conference
Conference | 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005 |
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Country | United States |
City | San Diego, CA |
Period | 20/06/2005 → 25/06/2005 |
Sponsor | IEEE Computer Society |
ID: 302055002