浙江大学学报（理学版）2025，Vol.52Issue(3)：334-345,12.DOI:10.3785/j.issn.1008-9497.2025.03.005
基于低秩张量和角度信息的多视图子空间聚类
Multi-view subspace clustering based on low-rank tensor and angular information
摘要
Abstract
Existing tensor-based multi-view subspace clustering methods have achieved remarkable success.However,these methods generally suffer from issues such as equivalent regularization of singular values and suboptimal construction of similarity matrices,which limit their performance.To address these challenges,a novel multi-view subspace clustering algorithm based on low-rank tensor and angular information(MSCLTAI)is proposed.First,in order to fully utilize the significant difference of singular values,a weighted tensor nuclear norm based on tensor singular value decomposition(t-SVD)is introduced to learn the coefficient matrices in multi-view subspace clustering.Meanwhile,low-rank constraints are imposed on the tensor superimposed by the coefficient matrices to explore higher-order relevant information in the multi-view data.Next,considering the consistency between different views and the importance of their angular information,the consistency regularization term and information fusion reduction strategy are designed to obtain more effective similarity matrices for spectral clustering.Finally,the effectiveness of the proposed algorithm is verified through comparisons with several state-of-the-art algorithms on five real-world datasets.关键词
多视图子空间聚类/低秩张量/一致性/角度信息Key words
multi-view subspace clustering/low-rank tensor/consistency/angular information分类
计算机与自动化引用本文复制引用
张沙沙,王长鹏..基于低秩张量和角度信息的多视图子空间聚类[J].浙江大学学报（理学版）,2025,52(3):334-345,12.基金项目
国家自然科学基金资助项目(12471480) （12471480）
长安大学中央高校基本科研业务费专项(300102122101). （300102122101）
