Robust Pseudo-Hierarchical Support Vector Clustering |
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| Abstract | Support vector clustering (SVC) has proven an efficient algorithm
for clustering of noisy and high-dimensional data sets, with
applications within many fields of research. An inherent problem,
however, has been setting the parameters of the SVC algorithm.
Using the recent emergence of a method for calculating the entire
regularization path of the support vector domain description, we
propose a fast method for robust pseudo-hierarchical support
vector clustering (HSVC). The method is demonstrated to work well
on generated data, as well as for detecting ischemic segments from
multidimensional myocardial perfusion magnetic resonance imaging data,
giving robust results while drastically reducing the need for parameter
estimation. |
| Keywords | Hierarchical support vector clustering |
| Type | Conference paper [With referee] |
| Conference | Scandinavian Conference on Image Analysis 2007 |
| Editors | |
| Year | 2007 |
| Publisher | Springer |
| Series | LNCS |
| BibTeX data | [bibtex] |
| IMM Group(s) | Image Analysis & Computer Graphics |