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WILF
2007
Springer

Possibilistic Clustering in Feature Space

11 years 7 days ago
Possibilistic Clustering in Feature Space
In this paper we propose the Possibilistic C-Means in Feature Space and the One-Cluster Possibilistic C-Means in Feature Space algorithms which are kernel methods for clustering in feature space based on the possibilistic approach to clustering. The proposed algorithms retain the properties of the possibilistic clustering, working as density estimator in feature space and showing high robustness to outliers, and in addition are able to model densities in the data space in a non-parametric way. One-Cluster Possibilistic C-Means in Feature Space can be seen also as a generalization of One-Class SVM.
Maurizio Filippone, Francesco Masulli, Stefano Rov
Added 09 Jun 2010
Updated 09 Jun 2010
Type Conference
Year 2007
Where WILF
Authors Maurizio Filippone, Francesco Masulli, Stefano Rovetta
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