Multiscale Hierarchical Support Vector Clustering

Multiscale Hierarchical Support Vector Clustering

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 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.
Keywords	statistical methods, segmentation, pattern recognition
Type	Conference paper [With referee]
Conference	International Symposium on Medical Imaging 2008
Year	2008 Month February
Publisher	{SPIE} Medical Imaging
BibTeX data	[bibtex]
IMM Group(s)	Image Analysis & Computer Graphics