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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Beyond pairwise clustering


    Contributors:
    Agarwal, S. (author) / Jongwoo Lim, (author) / Zelnik-Manor, L. (author) / Perona, P. (author) / Kriegman, D. (author) / Belongie, S. (author)


    Publication date :

    2005-01-01


    Size :

    183065 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Active Image Clustering with Pairwise Constraints from Humans

    Biswas, A. / Jacobs, D. | British Library Online Contents | 2014


    Coordinating Truck Platooning by Clustering Pairwise Fuel-Optimal Plans

    Hoef, Sebastian van de / Johansson, Karl Henrik / Dimarogonas, Dimos V. | IEEE | 2015


    Discovering Shape Classes using Tree Edit-Distance and Pairwise Clustering

    Torsello, A. / Robles-Kelly, A. / Hancock, E. R. | British Library Online Contents | 2007


    Temporal pattern mining of urban traffic volume data: a pairwise hybrid clustering method

    Taheri Sarteshnizi, Iman / Sarvi, Majid / Bagloee, Saeed Asadi et al. | Taylor & Francis Verlag | 2023

    Free access

    Pairwise face recognition

    Guo-Dong Guo, / Hong-Jiang Zhang, / Li, S.Z. | IEEE | 2001