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Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/6815
Title: Maximum margin clustering with multivariate loss function
Authors: Zhao, Bin
Kwok, James Tin-Yau
Zhang, Changshui
Keywords: Maximum margin clustering
Multivariate performance measure
Issue Date: 2009
Citation: Proceedings Ninth IEEE International Conference on Data Mining (ICDM '09), 6-9 December 2009, Miami, FL, USA, p. 637-646
Abstract: 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.
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URI: http://hdl.handle.net/1783.1/6815
Appears in Collections:CSE Conference Papers

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