南京大学学报（自然科学版）2026，Vol.62Issue(4)：607-628,22.DOI:10.13232/j.cnki.jnju.2026.04.008
基于动态锚点与质量感知混合专家的多视图聚类
Multi-view clustering with dynamic anchors and a quality-aware mixture of experts
摘要
Abstract
Multi-view clustering aims to exploit the consensus and complementarity across different views,yet existing methods face two major bottlenecks:first,global topology modeling relies on predefined similarity measures and fixed neighborhoods,making it difficult to adapt to complex data distributions;second,sample-level view quality varies significantly,and static weighting strategies fail to characterize fine-grained reliability changes,particularly lacking robustness in missing data scenarios.To this end,we propose a multi-view clustering framework based on Dynamic Anchors and quality-aware Mixture-of-Experts(DAMC-MoE).First,a learnable dynamic anchor mechanism replaces traditional predefined similarity measures,achieving end-to-end deep coupling between topological structure modeling and feature representation learning.Building on this,a quality-aware mixture-of-experts module is introduced,which generates quality tokens from sample-level completeness and signal-to-noise ratio to guide the gating mechanism for adaptive routing,realizing a paradigm shift from conventional view-level weighting to sample-level fine-grained perceptual fusion.Finally,a three-level contrastive learning mechanism is constructed to jointly reinforce semantic alignment from inter-view,intra-view,and local-global perspectives.In comprehensive comparative experiments on 5 benchmark datasets against 11 state-of-the-art algorithms,DAMC-MoE demonstrates superior clustering performance.Friedman test results further indicate that DAMC-MoE achieves significantly higher average rankings across three clustering evaluation metrics compared to all baseline methods.关键词
多视图聚类/动态锚点/混合专家/质量感知/对比学习Key words
multi-view clustering/dynamic anchors/mixture of experts/quality-aware/contrastive learning分类
信息技术与安全科学引用本文复制引用
李顺勇,赵婉婷,赵兴旺..基于动态锚点与质量感知混合专家的多视图聚类[J].南京大学学报（自然科学版）,2026,62(4):607-628,22.基金项目
国家自然科学基金(82274360),山西省基础研究计划(202303021221054,202403021211086),山西省留学回国人员科技活动择优项目(20250001),山西省回国留学人员科研项目(2024-002),山西省研究生教育创新计划(2025JG0006,2025SJ032) （82274360）
