基于动态锚点与质量感知混合专家的多视图聚类OA

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.