Unsupervised feature selection via unifying distribution alignment and structure preservation

doi:10.1016/j.inffus.2025.103544

	Unsupervised feature selection via unifying distribution alignment and structure preservation
	Cao, Chunxu 1; Zhang, Yong 2; Ai, Yanke 2,5; Zhang, Qiang 3,4
	2026-02-01
发表期刊	INFORMATION FUSION
ISSN	1566-2535
卷号	126 页码:20
摘要	The increasing complexity of high-dimensional data demands effective feature selection techniques that preserve both global distributional characteristics and local structures. However, existing methods often encounter a fundamental trade-off between preserving global distributional fidelity and maintaining local geometric structures, leading to information loss. This work presents a novel kernel-enhanced Gromov-Wasserstein alignment framework that unifies global distribution alignment and local structure preservation. Our approach leverages Gromov-Wasserstein distance and the kernel trick to enhance metric space comparisons, effectively capturing nonlinear relationships while improving stability in noisy data. To ensure scalability, we develop an efficient randomized filter algorithm, balancing computational efficiency with feature diversity. Extensive experiments across 20 benchmark datasets demonstrate the superior performance of our method, showing that it surpasses state-of-the-art feature selection techniques. These results highlight the effectiveness of integrating distributional alignment and structure preservation for unsupervised feature selection in high-dimensional data analysis.
关键词	Feature selection Filter feature selection method Distributional distances Gromov-Wasserstein distance Kernel trick Extreme gradient boosting
DOI	10.1016/j.inffus.2025.103544
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[12272054] ; Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science, BNU-HKBU United International College[2022B1212010006] ; Guangdong and Hong Kong Universities '1+1+1' Joint Research Collaboration Scheme
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods
WOS记录号	WOS:001548072500002
出版者	ELSEVIER
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/41763
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhang, Qiang
作者单位	1.Beijing Normal Univ, Sch Math Sci, Beijing 100875, Peoples R China 2.China Unicom, Unicom Digital Technol, Beijing 100013, Peoples R China 3.Beijing Normal Univ, Res Ctr Math, Zhuhai 519087, Peoples R China 4.Beijing Normal Hong Kong Baptist Univ, Guangdong Prov Key Lab Interdisciplinary Res & App, Zhuhai 519087, Peoples R China 5.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Cao, Chunxu,Zhang, Yong,Ai, Yanke,et al. Unsupervised feature selection via unifying distribution alignment and structure preservation[J]. INFORMATION FUSION,2026,126:20.
APA	Cao, Chunxu,Zhang, Yong,Ai, Yanke,&Zhang, Qiang.(2026).Unsupervised feature selection via unifying distribution alignment and structure preservation.INFORMATION FUSION,126,20.
MLA	Cao, Chunxu,et al."Unsupervised feature selection via unifying distribution alignment and structure preservation".INFORMATION FUSION 126(2026):20.