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张宪超
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主要任职: 科学技术研究院国防重大项目办公室主任

性别: 男

毕业院校: 中国科技大学

学位: 博士

所在单位: 软件学院、国际信息与软件学院

学科: 计算机应用技术. 软件工程

电子邮箱: xczhang@dlut.edu.cn

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Constrained Clustering With Nonnegative Matrix Factorization

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论文类型: 期刊论文

第一作者: Zhang, Xianchao

通讯作者: Zhang, XC (reprint author), Dalian Univ Technol, Sch Software, Dalian 116620, Peoples R China.

合写作者: Zong, Linlin,Liu, Xinyue,Luo, Jiebo

发表时间: 2016-07-01

发表刊物: IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS

收录刊物: SCIE、EI

卷号: 27

期号: 7

页面范围: 1514-1526

ISSN号: 2162-237X

关键字: Constrained clustering; nonnegative matrix factorization (NMF); semi-supervised learning; symmetric NMF (SymNMF)

摘要: Nonnegative matrix factorization (NMF) and symmetric NMF (SymNMF) have been shown to be effective for clustering linearly separable data and nonlinearly separable data, respectively. Nevertheless, many practical applications demand constrained algorithms in which a small number of constraints in the form of must-link and cannot-link are available. In this paper, we propose an NMF-based constrained clustering framework in which the similarity between two points on a must-link is enforced to approximate 1 and the similarity between two points on a cannot-link is enforced to approximate 0. We then formulate the framework using NMF and SymNMF to deal with clustering of linearly separable data and nonlinearly separable data, respectively. Furthermore, we present multiplicative update rules to solve them and show the correctness and convergence. Experimental results on various text data sets, University of California, Irvine (UCI) data sets, and gene expression data sets demonstrate the superiority of our algorithms over existing constrained clustering algorithms.

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