南京航空航天大学学报（自然科学版）2026，Vol.58Issue(2)：457-470,14.DOI:10.16356/j.2097-6771.2026.02.022
基于双通道粒计算的深度多视图聚类方法
Deep Multi-view Clustering with Dual-Channel Granular Computing
摘要
Abstract
In order to deal with the problems of quality differences among different views,ambiguous boundary samples and differences in semantic structures among different views,we propose deep multi-view clustering with dual-channel granular computing.A dual-channel feature fusion module is designed to strengthen key representations,where the global average pooling channel captures holistic semantics,and the global max pooling channel focuses on highly discriminative cues.Furthermore,a dual-channel contrast learning strategy is introduced for contrast learning at the sample and local fuzzy granular-ball structure level respectively.Fuzzy granular-ball level contrast learning is divided into intra-granular-ball and cross-view fuzzy granular-ball contrast learning.The former optimizes the clustering boundary by making positive samples inside the granular-ball closer.The latter ensures consistent granular-ball structures are learned across different views.Additionally,this paper introduces a view-adaptive attention weight assignment mechanism that enhances the leading role of high-quality views in clustering.We verify the effectiveness of our method on eight publicly available multi-view datasets.The results show that our method improves clustering accuracy compared to the existing multi-view clustering methods,such as MFLVC,SCMVC,etc.关键词
深度多视图聚类/双通道对比学习/跨视图模糊粒球/视图自适应注意力权重分配/双通道特征融合/粒计算Key words
deep multi-view clustering/dual-channel contrastive learning/cross-view fuzzy granular-ball/view-adaptive attention weight assignment/dual-channel feature fusion/granular computing分类
信息技术与安全科学引用本文复制引用
蔡超越,马星如,郭静,胡鑫,鞠恒荣,丁卫平..基于双通道粒计算的深度多视图聚类方法[J].南京航空航天大学学报（自然科学版）,2026,58(2):457-470,14.基金项目
国家自然科学基金(62006128) （62006128）
南京大学计算机软件新技术国家重点实验室资助项目(KFKT2024B30) （KFKT2024B30）
南通市自然科学基金(JC2024044) （JC2024044）
教育部产学合作协同育人项目(2409233550). （2409233550）
