Maximum Margin Clustering with Multivariate Loss Function

被引：106

作者：

Zhao, Bin ^{[1
]}

Kwok, James ^{[2
]}

Zhang, Changshui ^{[1
]}

机构：

[1] Tsinghua Univ, Dept Automat, Tsinghua Natl Lab Informat Sci & Technol TNList, State Key Lab Intelligent Technol & Syst, Beijing 100084, Peoples R China

[2] Hong Kong Univ Sci & Technol, Dept Comp Sci & Engn, Hong Kong, Peoples R China

来源：

2009 9TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING | 2009年

基金：

中国国家自然科学基金;

关键词：

D O I：

10.1109/ICDM.2009.37

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：637 / +

页数：3