SEMANTICS-GUIDED DISENTANGLED MULTI-VIEW CLUSTERING

Existing deep multi-view clustering methods commonly adopt dual-branch paradigms to extract and fuse view-shared and view-specific features, yet they suffer from inherent structural limitations. Direct aggregation of these features introduces view-irrelevant noise and underutilizes cross-view complementary information, while partitioning features by view origins does not align with clustering objectives, due to redundant view-shared features and underexploited discriminative cues in view-specific features. To address these issues, we propose a Semantics-Guided Disentangled paradigm for Multi-View Clustering (SG-DMVC). Specifically, we explicitly decompose the input of each view into discriminative cluster-relevant features and task-agnostic cluster-irrelevant features. The view-wise cluster-relevant features are aggregated via log-space probability consensus to yield robust cross-view shared semantics, which are further regularized by dual-level topological alignment to preserve global manifold distribution consistency and refine local sample-pair structures. Meanwhile, covariance decorrelation regularization is imposed to enforce second-order decorrelation between cluster-relevant and cluster-irrelevant features, effectively eliminating irrelevant feature interference and latent information leakage. Extensive experiments on both complete and incomplete multi-view datasets show that the proposed SG-DMVC yields superior clustering performance and strong robustness against view missing compared with state-of-the-art methods.