Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/6815

Maximum margin clustering with multivariate loss function

Authors Zhao, B.
Kwok, J.
Zhang, C.
Issue Date 2009
Source Proceedings - IEEE International Conference on Data Mining, ICDM, 2009, p. 637-646
Summary This paper presents a simple but powerful extension of the maximum margin clustering (MMC) algorithm that optimizes multivariate performance measure specifically defined for clustering, including Normalized Mutual Information, Rand Index and F-measure. Different from previous MMC algorithms that always employ the error rate as the loss function, our formulation involves a multivariate loss function that is a non-linear combination of the individual clustering results. Computationally, we propose a cutting plane algorithm to approximately solve the resulting optimization problem with a guaranteed accuracy. Experimental evaluations show clear improvements in clustering performance of our method over previous maximum margin clustering algorithms. © 2009 IEEE.
Subjects
ISSN 1550-4786
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Language English
Format Conference paper
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