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.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Multiclass spectral clustering


    Beteiligte:
    Yu, (Autor:in) / Shi, (Autor:in)


    Erscheinungsdatum :

    2003-01-01


    Format / Umfang :

    534501 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Multiclass Spectral Clustering

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


    MULTICLASS FREIGHT CAR

    SHISHKIN ANDREI VLADIMIROVICH | Europäisches Patentamt | 2021

    Freier Zugriff

    Tiefergelegt - Fahrbericht Setra Multiclass NF

    Unruh,R. / EvoBus,Stuttgart,DE | Kraftfahrwesen | 2007


    Multi ( C ) Klassentreffen - Setra Multiclass

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


    Verpackungs Kuenstler: Setra Multiclass NF

    Unruh,R. / Setra,Ulm,DE | Kraftfahrwesen | 2006