A multi-kernel spectral clustering algorithm based on incomplete views

被引:0
|
作者
Zhang, Wei [1 ]
Yang, Yan [1 ]
Hu, Jie [1 ]
机构
[1] Southwest Jiaotong Univ, Sch Informat Sci & Technol, Chengdu 611756, Sichuan, Peoples R China
来源
DATA SCIENCE AND KNOWLEDGE ENGINEERING FOR SENSING DECISION SUPPORT | 2018年 / 11卷
基金
美国国家科学基金会;
关键词
Multi-view clustering; spectral clustering; multi-kernel; incomplete view;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
With the diversity of data sources, multi-view clustering algorithms are widely used. The traditional research routinely assumes that the multi-view data is complete, but the existing data may actually be missing. So the incomplete view clustering has become a hot research topic. In this paper, a multi-kernel spectral clustering algorithm based on incomplete views (IVMKSpec) is put forward. Firstly, the incomplete datasets are constructed with 10% to 90% of the loss rate, where they are clustered with the estimation of kernel and spectral clustering, and then the clustering results are evaluated by NMI and F-measure. Multi-kernel learning overcomes the defect that single kernel can effectively not handle data of heterogeneous and multiple data sources. Moreover, the multi-kernel spectral clustering is applied to incomplete datasets which improves the performance of incomplete clustering. Finally, the experimental results demonstrate that the proposed algorithm is robust and effective in most datasets.
引用
收藏
页码:477 / 484
页数:8
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