Multiscale hierarchical support vector clustering

Michael Saas Hansen; David Alberg Holm; Karl Sjöstrand; Carsten Dan Ley; Ian John Rowland; Rasmus Larsen

doi:10.1117/12.771027

26 March 2008 Multiscale hierarchical support vector clustering

Michael Saas Hansen, David Alberg Holm, Karl Sjöstrand, Carsten Dan Ley, Ian John Rowland, Rasmus Larsen

Proceedings Volume 6914, Medical Imaging 2008: Image Processing; 69144B (2008) https://doi.org/10.1117/12.771027
Event: Medical Imaging, 2008, San Diego, California, United States

Abstract

Clustering 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 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 reduced, compared to support vector clustering.

Citation Download Citation

Michael Saas Hansen, David Alberg Holm, Karl Sjöstrand, Carsten Dan Ley, Ian John Rowland, and Rasmus Larsen "Multiscale hierarchical support vector clustering", Proc. SPIE 6914, Medical Imaging 2008: Image Processing, 69144B (26 March 2008); https://doi.org/10.1117/12.771027

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