Multiscale hierarchical support vector clustering
Michael Sass Hansen, David Alberg Holm, Karl Sjöstrand, Carsten Dan Ley, Ian J. Rowland, Rasmus Larsen · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2008
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.