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ICDM
2009
IEEE

Maximum Margin Clustering with Multivariate Loss Function

7 years 3 months ago
Maximum Margin Clustering with Multivariate Loss Function
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.
Bin Zhao, James Tin-Yau Kwok, Changshui Zhang
Added 18 Feb 2011
Updated 18 Feb 2011
Type Journal
Year 2009
Where ICDM
Authors Bin Zhao, James Tin-Yau Kwok, Changshui Zhang
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