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Soft clustering using weighted one-class support vector machines

Bicego, Manuele and Figueiredo, Mario A. T. (2009) Soft clustering using weighted one-class support vector machines. Pattern Recognition, Vol. 42 (1), p. 27-32. ISSN 0031-3203. Article.

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DOI: 10.1016/j.patcog.2008.07.004


This paper describes a new soft clustering algorithm in which each cluster is modelled by a one-class 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 methods.

Item Type:Article
ID Code:1403
Uncontrolled Keywords:Soft clustering, one-class support vector machines, EM-like algorithms, kernel methods, deterministic annealing
Subjects:Area 09 - Ingegneria industriale e dell'informazione > ING-INF/05 Sistemi di elaborazione delle informazioni
Divisions:001 Università di Sassari > 01 Dipartimenti > Economia, impresa, regolamentazione
Publisher:Pergamon / Elsevier
Copyright Holders:© 2008 Elsevier
Deposited On:18 Aug 2009 10:05

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