Robust Pseudo-Hierarchical Support Vector Clustering

Michael Sass Hansen, Karl Sjöstrand, Hildur Olafsdóttir, Henrik B. W. Larsson, Mikkel Bille Stegmann, Rasmus Larsen, Bjarne Kjær Ersbøll (Editor), Janne Heikkilä (Editor), Ivar Austvoll (Editor), Ingela Nyström (Editor)

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


    Support vector clustering (SVC) has proven an efficient algorithm for clustering of noisy and high-dimensional data sets, with applications within many fields of research. An inherent problem, however, has been setting the parameters of the SVC algorithm. Using the recent emergence of a method for calculating the entire regularization path of the support vector domain description, we propose a fast method for robust pseudo-hierarchical support vector clustering (HSVC). The method is demonstrated to work well on generated data, as well as for detecting ischemic segments from multidimensional myocardial perfusion magnetic resonance imaging data, giving robust results while drastically reducing the need for parameter estimation.
    Original languageEnglish
    Title of host publicationScandinavian Conference on Image Analysis 2007
    Publication date2007
    Publication statusPublished - 2007
    Event15th Scandinavian Conference on Image Analysis (SCIA) - Aalborg, Denmark
    Duration: 1 Jan 2007 → …


    Conference15th Scandinavian Conference on Image Analysis (SCIA)
    Period01/01/2007 → …


    • Hierarchical support vector clustering


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