Parameter-Free Weighted Multi-View Projected Clustering with Structured Graph Learning

被引:60
作者
Wang, Rong [1 ]
Nie, Feiping [1 ,2 ]
Wang, Zhen [1 ]
Hu, Haojie [3 ]
Li, Xuelong [1 ,2 ]
机构
[1] Northwestern Polytech Univ, Ctr OPT IMagery Anal & Learning OPTIMAL, Xian 710072, Peoples R China
[2] Northwestern Polytech Univ, Sch Comp Sci, Xian 710072, Peoples R China
[3] Xian Res Inst Hitech, Xian 710025, Peoples R China
基金
中国国家自然科学基金;
关键词
Clustering methods; Task analysis; Dimensionality reduction; Laplace equations; Visualization; Clustering algorithms; Biomedical optical imaging; Multi-view clustering; dimensionality reduction; structured graph learning; PROPAGATION; FRAMEWORK; SCALE;
D O I
10.1109/TKDE.2019.2913377
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In many real-world applications, we are often confronted with high dimensional data which are represented by various heterogeneous views. How to cluster this kind of data is still a challenging problem due to the curse of dimensionality and effectively integration of different views. To address this problem, we propose two parameter-free weighted multi-view projected clustering methods which perform structured graph learning and dimensionality reduction simultaneously. We can use the obtained structured graph directly to extract the clustering indicators, without performing other discretization procedures as previous graph-based clustering methods have to do. Moreover, two parameter-free strategies are adopted to learn an optimal weight for each view automatically, without introducing a regularization parameter as previous methods do. Extensive experiments on several public datasets demonstrate that the proposed methods outperform other state-of-the-art approaches and can be used more practically.
引用
收藏
页码:2014 / 2025
页数:12
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