Robust pseudo-hierarchical support vector clustering

Authors:
Michael Sass Hansen;Karl Sjöstrand;Hildur Ólafsdóttir;Henrik B. W. Larsson;Mikkel B. Stegmann;Rasmus Larsen
Affiliations:
Informatics and Mathematical Modelling, Technical University of Denmark, Lyngby, Denmark;Informatics and Mathematical Modelling, Technical University of Denmark, Lyngby, Denmark;Informatics and Mathematical Modelling, Technical University of Denmark, Lyngby, Denmark;Hospital of Glostrup, Glostrup, Denmark;3shape A/S, Copenhagen, Denmark;3shape A/S, Copenhagen, Denmark
Venue:
SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
Year:
2007

Citing 3
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Abstract

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.