Low-rank tensor completion via tensor tri-factorization and sparse transformation

doi:10.1016/j.sigpro.2025.109935

	Low-rank tensor completion via tensor tri-factorization and sparse transformation
	Yang, Fanyin 1; Zheng, Bing 1; Zhao, Ruijuan2
	2025-08
在线发表日期	2025-02
发表期刊	SIGNAL PROCESSING
卷号	233
摘要	Low-rank tensor factorization techniques have gained significant attention in low-rank tensor completion (LRTC) tasks due to their ability to reduce computational costs while maintaining the tensor's low-rank structure. However, existing methods often overlook the significance of tensor singular values and the sparsity of the tensor's third-mode fibers in the transformation domain, leading to an incomplete capture of both the low-rank structure and the inherent sparsity, which limits recovery accuracy. To address these issues, we propose a novel tensor tri-factorization logarithmic norm (TTF-LN) that more effectively captures the low-rank structure by emphasizing the significance of tensor singular values. Building on this, we introduce the tensor tri-factorization with sparse transformation (TTF-ST) model for LRTC, which integrates both low-rank and sparse priors to improve accuracy of incomplete tensor recovery. The TTF-ST model incorporates a sparse transformation that represents the tensor as the product of a low-dimensional sparse representation tensor and a compact orthogonal matrix, which extracts sparsity while reducing computational complexity. To solve the proposed model, we design an optimization algorithm based on the alternating direction method of multipliers (ADMM) and provide a rigorous theoretical analysis. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art methods in both recovery accuracy and computational efficiency.
关键词	Low-rank tensor completion Tensor tubal rank Tensor tri-factorization logarithmic norm Sparse transformation
DOI	10.1016/j.sigpro.2025.109935
收录类别	SCIE ; EI
ISSN	0165-1684
语种	英语
WOS研究方向	Engineering
WOS类目	Engineering, Electrical & Electronic
WOS记录号	WOS:001428658200001
出版者	ELSEVIER
EI入藏号	20250817891975
EI主题词	Tensors
EI分类号	1106.6 Data Analytics ; 1201.1 Algebra and Number Theory ; 1201.14 Geometry and Topology ; 1201.4 Applied Mathematics
原始文献类型	Article
EISSN	1872-7557
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/38810
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, Gansu, Peoples R China; 2.Lanzhou Univ Finance & Econ, Sch Informat Engn, Lanzhou 730101, Gansu, Peoples R China
推荐引用方式 GB/T 7714	Yang, Fanyin,Zheng, Bing,Zhao, Ruijuan. Low-rank tensor completion via tensor tri-factorization and sparse transformation[J]. SIGNAL PROCESSING,2025,233.
APA	Yang, Fanyin,Zheng, Bing,&Zhao, Ruijuan.(2025).Low-rank tensor completion via tensor tri-factorization and sparse transformation.SIGNAL PROCESSING,233.
MLA	Yang, Fanyin,et al."Low-rank tensor completion via tensor tri-factorization and sparse transformation".SIGNAL PROCESSING 233(2025).