We propose a principled account on multiclass spectral clustering. Given a discrete clustering formulation, we first solve a relaxed continuous optimization problem by eigen-decomposition. We clarify the role of eigenvectors as a generator of all optimal solutions through orthonormal transforms. We then solve an optimal discretization problem, which seeks a discrete solution closest to the continuous optima. The discretization is efficiently computed in an iterative fashion using singular value decomposition and nonmaximum suppression. The resulting discrete solutions are nearly global-optimal. Our method is robust to random initialization and converges faster than other clustering methods. Experiments on real image segmentation are reported.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Multiclass spectral clustering


    Contributors:
    Yu, (author) / Shi, (author)


    Publication date :

    2003-01-01


    Size :

    534501 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Multiclass Spectral Clustering

    Yu, S. / Shi, J. / IEEE | British Library Conference Proceedings | 2003


    MULTICLASS FREIGHT CAR

    SHISHKIN ANDREI VLADIMIROVICH | European Patent Office | 2021

    Free access

    Tiefergelegt - Fahrbericht Setra Multiclass NF

    Unruh,R. / EvoBus,Stuttgart,DE | Automotive engineering | 2007


    Multi ( C ) Klassentreffen - Setra Multiclass

    Boehnke,S. / Setra,Neu-Ulm,DE | Automotive engineering | 2016


    Verpackungs Kuenstler: Setra Multiclass NF

    Unruh,R. / Setra,Ulm,DE | Automotive engineering | 2006