Adaptive partial graph learning and fusion for incomplete multi-view clustering

被引:8
|
作者
Zheng, Xiao [1 ]
Liu, Xinwang [1 ]
Chen, Jiajia [2 ]
Zhu, En [1 ]
机构
[1] Natl Univ Def Technol, Sch Comp Sci, Changsha 410073, Peoples R China
[2] Xuzhou Med Univ, Peoples Hosp Huaian 2, Affiliated Huaian Hosp, Dept Pharm, Huaian, Peoples R China
基金
中国国家自然科学基金;
关键词
clustering; incomplete data clustering; multiple view clustering; SCALE;
D O I
10.1002/int.22655
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Most of existing multi-view clustering methods assume that different feature views of data are fully observed. However, it is common that only portions of data features can be obtained in many practical applications. The presence of incomplete feature views hinders the performance of the conventional multi-view clustering methods to a large extent. Recently proposed incomplete multi-view clustering methods often focus on directly learning a common representation or a consensus affinity similarity graph from available feature views while ignore the valuable information hidden in the missing views. In this study, we present a novel incomplete multi-view clustering method via adaptive partial graph learning and fusion (APGLF), which can capture the local data structure of both within-view and cross-view. Specifically, we use the available data of each view to learn a corresponding view-specific partial graph, in which the within-view local structure can be well preserved. Then we design a cross-view graph fusion term to learn a consensus complete graph for different views, which can take advantage of the complementary information hidden in the view-specific partial graphs learned from incomplete views. In addition, a rank constraint is imposed on the graph Laplacian matrix of the fused graph to better recover the optimal cluster structure of original data. Therefore, APGLF integrates within-view partial graph learning, cross-view partial graph fusion and cluster structure recovering into a unified framework. Experiments on five incomplete multi-view data sets are conducted to validate the efficacy of APGLF when compared with eight state-of-the-art methods.
引用
收藏
页码:991 / 1009
页数:19
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