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

机构：[1] School of Mathematical Sciences, Guizhou Normal University, Guiyang, 550025, China; [2] School of Big Data, Laboratory of Electrical Power Big, Data of Guizhou Province, Guizhou Institute of Technology, Guiyang, 550025, China; [3] School of Mathematical Sciences, Xiamen University, Xiamen, 361005, China; [4] Department of Automation, Moutai Institute, Renhuai, 564507, China

第一机构：School of Mathematical Sciences, Guizhou Normal University, Guiyang, 550025, China

通信机构：School of Mathematical Sciences, Guizhou Normal University, Guiyang, 550025, China

年份：2026

卷号：672

外文期刊名：Neurocomputing

收录：EI(收录号：20260519982155);Scopus(收录号：2-s2.0-105028474632)

语种：英文

外文关键词：Benchmarking - Cluster analysis - Clustering algorithms - Matrix factorization

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 simultaneously 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 smoothing 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. ? 2026 Elsevier B.V.