文献类型：期刊文献

英文题名：Dual hypergraph regularized nonnegative matrix factorization with nonsmooth and orthogonality constraints for data clustering

作者：Song, Chunli Lu, Linzhang Zeng, Chengbin

第一作者：Song, Chunli;宋春丽

通信作者：Lu, LZ[1]

机构：[1]Guizhou Normal Univ, Sch Math Sci, Guiyang 550025, Peoples R China;[2]Guizhou Inst Technol, Sch Big Data, Lab Elect Power Big Data Guizhou Prov, Guiyang 550025, Peoples R China;[3]Xiamen Univ, Sch Math Sci, Xiamen 361005, Peoples R China;[4]Moutai Inst, Dept Automat, Renhuai 564507, Peoples R China

第一机构：Guizhou Normal Univ, Sch Math Sci, Guiyang 550025, Peoples R China

通信机构：corresponding author), Guizhou Normal Univ, Sch Math Sci, Guiyang 550025, Peoples R China.

年份：2026

卷号：672

外文期刊名：NEUROCOMPUTING

收录：;EI(收录号：20260519982155);WOS:【SCI-EXPANDED(收录号:WOS:001680469100001)】；

基金：This work was supported by the National Natural Science Foundation of China (12161020, 12061025) , the Guizhou Provincial Basic Research Program (Natural Science) (QKHJC-ZK [2024] YB528) , the Science and Technology Program of Guizhou Province (KXJZ [2024] 002, CXTD [2025] 024) , the Key Laboratory of New Power System Operation Control of Guizhou Province (Qiankehe Platform ZSYS [2025] 007) , the Zunyi Science and Technology Plan Project (Zunshi Kehe HZ Zi (2024) No. 385) , the Science Research Foundation for High-level Talents of Moutai Institute (mygccrc [2024] 012) , and the Natural Science Research Project of Guizhou Provincial Department of Education (No. QJJ [2023] 011) .

语种：英文

外文关键词：Nonnegative matrix factorization; Dual hypergraph regularization; Nonsmooth constraint; Orthogonality; Data clustering

摘要：Nonnegative Matrix Factorization (NMF) has emerged as a powerful tool for data clustering, largely due to its ability to yield interpretable low-dimensional representations. However, existing NMF-based methods struggle to fully model high-order relationships across both sample and feature spaces, and they also fail to simultane ously enforce feature sparsity and preserve intrinsic geometric structures, which are key factors for clustering complex datasets. To address these challenges, this paper proposes a novel framework, namely Dual Hypergraph Regularized Nonsmooth Nonnegative Matrix Factorization with Orthogonality Constraints (DHNNMF). The model employs dual hypergraph regularization to capture high-order correlations, a nonsmooth constraint via a smooth ing matrix to enhance feature sparsity and interpretability, and orthogonality constraints on the factor matrices to prevent degenerate solutions and improve clustering quality. An efficient multiplicative optimization algorithm is developed for the non-convex objective function, supported by rigorous theoretical analysis that guarantees monotonic convergence. Extensive experiments on benchmark datasets demonstrate that DHNNMF achieves superior or comparable performance compared to baseline methods.