Auto-Weighted Multiple Graph Regularized Non-negative Tensor Tucker Decomposition for Clustering

doi:10.1007/s10915-025-02817-0

	Auto-Weighted Multiple Graph Regularized Non-negative Tensor Tucker Decomposition for Clustering
	Liu, Guimin 1; Zhao, Ruijuan2 ; Zheng, Bing 1; Yang, Fanyin 1
	2025-03
发表期刊	JOURNAL OF SCIENTIFIC COMPUTING
卷号	102 期号:3
摘要	Non-negative Tucker decomposition (NTD) has received much attention due to its efficient processing of high-dimensional non-negative data. To preserve the intrinsic geometric structure of the data, various graph regularization NTD methods have been proposed. However, most existing methods rely on single graph regularization, limiting their flexibility and adaptability, since a single graph may not adequately capture the intrinsic manifold structure of various datasets. To address this problem, this paper introduces an auto-weighted multiple graph structure as the regularizer for NTD, and then proposes a novel method called auto-weighted multiple graph regularized non-negative Tucker decomposition (AMGRNTD). The AMGRNTD method utilizes a linear combination of multiple simple graphs to more effectively preserve the intrinsic manifold structure of the original data, offering greater applicability to practical problems than single graph-based methods. Furthermore, the AMGRNTD method automatically learns an optimal weight for each graph without additional parameters. Experimental results on four real-world datasets demonstrate that the proposed method achieves better performance in image clustering than some existing state-of-the-art graph-based regularization methods.
关键词	Non-negative tensor Tucker decomposition Multiple graph Clustering
DOI	10.1007/s10915-025-02817-0
收录类别	SCIE ; EI
ISSN	0885-7474
语种	英语
WOS研究方向	Mathematics
WOS类目	Mathematics, Applied
WOS记录号	WOS:001415322600005
出版者	SPRINGER/PLENUM PUBLISHERS
EI入藏号	20250817923571
EI主题词	Tensors
EI分类号	1106.2 Data Handling and Data Processing ; 1201.1 Algebra and Number Theory ; 1201.14 Geometry and Topology ; 1201.4 Applied Mathematics ; 1201.8 Discrete Mathematics and Combinatorics, Includes Graph Theory, Set Theory
原始文献类型	Article
EISSN	1573-7691
引用统计	被引频次[WOS]：0 [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.lzufe.edu.cn/handle/39EH0E1M/38808
专题	信息工程与人工智能学院
通讯作者	Zheng, Bing
作者单位	1.Lanzhou Univ, Sch Math & Stat, Lanzhou 730000, Peoples R China; 2.Lanzhou Univ Finance & Econ, Sch Informat Engn & Artificial Intelligence, Lanzhou 730000, Peoples R China
推荐引用方式 GB/T 7714	Liu, Guimin,Zhao, Ruijuan,Zheng, Bing,et al. Auto-Weighted Multiple Graph Regularized Non-negative Tensor Tucker Decomposition for Clustering[J]. JOURNAL OF SCIENTIFIC COMPUTING,2025,102(3).
APA	Liu, Guimin,Zhao, Ruijuan,Zheng, Bing,&Yang, Fanyin.(2025).Auto-Weighted Multiple Graph Regularized Non-negative Tensor Tucker Decomposition for Clustering.JOURNAL OF SCIENTIFIC COMPUTING,102(3).
MLA	Liu, Guimin,et al."Auto-Weighted Multiple Graph Regularized Non-negative Tensor Tucker Decomposition for Clustering".JOURNAL OF SCIENTIFIC COMPUTING 102.3(2025).