郑州大学学报（理学版）2025，Vol.57Issue(4)：30-39,10.DOI:10.13705/j.issn.1671-6841.2023205
基于自适应融合全局和局部信息的锚点多视图聚类
Anchor Multi-view Clustering Based on Adaptive Fusion of Global and Local Information
摘要
Abstract
Subspace-based multi-view clustering algorithms have attracted much attention due to their good clustering performance and mathematical interpretability.Among them,some large-scale multi-view subspace clustering algorithms based on anchor strategy can effectively reduce the spatiotemporal com-plexity.However,existing algorithms often learned the subspace self-representation matrix from the global structure,ignoring the local structure information between the view data,anchors and the subspace self-representation matrices.Inspired by the multi-view self-weighted multi-graph learning algorithm,the an-chor multi-view clustering based on adaptive fusion of global and local information(AMVC-AFGL)algo-rithm was proposed.The proposed algorithm aimed to learn a more effective subspace anchor graph matrix for each view data by adaptively allocating view weights and fusing the global information and local infor-mation between the data,and then concatenated them into a smaller fusion anchor graph matrix for spec-tral clustering.Extensive experiments were carried out on 10 public real benchmark datasets,and com-pared with other 12 advanced multi-view clustering algorithms,the results showed the effectiveness and scalability of the proposed algorithm.关键词
多视图聚类/自加权/锚点/子空间聚类/谱聚类/大规模Key words
multi-view clustering/self-weighted/anchor/subspace clustering/spectral clustering/large-scale分类
计算机与自动化引用本文复制引用
冉戆,王思为,祝恩..基于自适应融合全局和局部信息的锚点多视图聚类[J].郑州大学学报（理学版）,2025,57(4):30-39,10.基金项目
国家自然科学基金项目(62276271,62325604) （62276271,62325604）
