Robust self-tuning multi-view clustering

被引:10
|
作者
Yuan, Changan [1 ]
Zhu, Yonghua [1 ]
Zhong, Zhi [2 ]
Zheng, Wei [1 ]
Zhu, Xiaofeng [1 ,3 ]
机构
[1] Guangxi Acad Sci, Nanning 540000, Peoples R China
[2] Nanning Normal Univ, Nanning 540001, Peoples R China
[3] Guangxi Normal Univ, Guilin 541004, Peoples R China
来源
WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS | 2022年 / 25卷 / 02期
基金
中国国家自然科学基金;
关键词
Multi-view clustering; Robust statistics; Initialization sensitivity; Cluster number determination; Outlier detection; HALF-QUADRATIC MINIMIZATION; K-MEANS; CONNECTIVITY;
D O I
10.1007/s11280-021-00945-9
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Previous methods of multi-view clustering focused on the improvement of clustering effectiveness by detecting common information of all views and individual information for every view, but they ignore the following issues, i.e., the initialization sensitivity, the cluster number determination, and the influence of outliers. However, either single-view clustering or multi-view clustering often suffers from above issues. In this paper, we propose a robust self-tuning multi-view clustering to introduce a sum-of-norm loss function to explore the issue of initialization sensitivity, design a sum-of-norm regularization to automatically determine the cluster number, and employ robust statistics techniques to reduce influence of outliers. Furthermore, we propose an effective alternating optimization method to solve the resulting objective function and then theoretically prove its convergence. Experimental results on both synthetic and real data sets demonstrated that our proposed multi-view clustering method outperformed the state-of-the-art clustering methods, in terms of four clustering evaluation metrics.
引用
收藏
页码:489 / 512
页数:24
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