Creating and sharing knowledge for telecommunications

Soft clustering using weighted one-class support vector machines

Bicego, M. ; Figueiredo, M. A. T.

Pattern Recognition Vol. 42, Nº 1, pp. 27 - 32, January, 2009.

ISSN (print): 0031-3203
ISSN (online):

Scimago Journal Ranking: 1,16 (in 2009)

Digital Object Identifier: 10.1016/j.patcog.2008.07.004

This paper describes a new soft clustering algorithm in which each cluster is modelled by a oneclass support vector machine
(OC-SVM). The proposed algorithm extends a previously
proposed hard clustering algorithm, also based on OC-SVM
representation of clusters. The key building block of our
method is the weighted OC-SVM (WOC-SVM), a novel tool
introduced in this paper, based on which an expectation–
maximization-type soft clustering algorithm is defined. A
deterministic annealing version of the algorithm is also
introduced, and shown to improve the robustness with respect to
initialization. Experimental results show that the proposed soft
clustering algorithm outperforms its hard clustering
counterpart, namely in terms of robustness with respect
to initialization, as well as several other state-of-the-art