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Proceedings Paper

Multiscale hierarchical support vector clustering
Author(s): Michael Saas Hansen; David Alberg Holm; Karl Sjöstrand; Carsten Dan Ley; Ian John Rowland; Rasmus Larsen
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Paper 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.

Paper Details

Date Published: 26 March 2008
PDF: 10 pages
Proc. SPIE 6914, Medical Imaging 2008: Image Processing, 69144B (26 March 2008); doi: 10.1117/12.771027
Show Author Affiliations
Michael Saas Hansen, Technical Univ. of Denmark (Denmark)
David Alberg Holm, Technical Univ. of Denmark (Denmark)
Copenhagen Univ. Hospital (Denmark)
Karl Sjöstrand, Technical Univ. of Denmark (Denmark)
Carsten Dan Ley, Institute for Molecular Pathology (Denmark)
Ian John Rowland, Copenhagen Univ. Hospital (Denmark)
Rasmus Larsen, Technical Univ. of Denmark (Denmark)

Published in SPIE Proceedings Vol. 6914:
Medical Imaging 2008: Image Processing
Joseph M. Reinhardt; Josien P. W. Pluim, Editor(s)

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