Multiscale Hierarchical Support Vector Clustering

AbstractClustering is the preferred choice of method in many applications, and support vector clustering (SVC) has proven efficient for clustering noisy and high-dimensional data sets.
A method for multiscale hierarchical support vector clustering is demonstrated, using the recently emerged method for fast calculation of the entire regularization path of the support vector domain description. The method is illustrated on artificially generated examples, and applied for detecting blood vessels from high resolution time series of magnetic resonance imaging data. The obtained results are robust while the need for parameter estimation is vastly reduced, compared to support vector clustering.
Keywordsstatistical methods, segmentation, pattern recognition
TypeConference paper [With referee]
ConferenceInternational Symposium on Medical Imaging 2008
Year2008    Month February
PublisherSPIE Medical Imaging
AddressRichard Petersens Plads, Building 321, DK-2800 Kgs. Lyngby
BibTeX data [bibtex]
IMM Group(s)Image Analysis & Computer Graphics