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Sphere packing for clustering sets of vectors in feature space

    Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

    2 Citations (Scopus)

    Abstract

    We propose a method for clustering sets of vectors by packing spheres learnt to represent the support of the different sets. The algorithm can work efficiently in a kernel-induced feature space by using the kernel trick. Experimental results on synthetic and real-world datasets show that the proposal is competitive with the state of the art.

    Original languageEnglish
    Title of host publicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
    Pages2092-2095
    Number of pages4
    DOIs
    Publication statusPublished - 18 Aug 2011
    Event2011 (36th) IEEE International Conference on Acoustics, Speech, and Signal Processing - Prague, Czech Republic
    Duration: 22 May 2011 → 27 May 2011

    Conference

    Conference2011 (36th) IEEE International Conference on Acoustics, Speech, and Signal Processing
    Abbreviated titleICASSP '11
    Country/TerritoryCzech Republic
    CityPrague
    Period22/05/11 → 27/05/11

    Keywords

    • Clustering
    • kernel methods
    • sequences

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