Incomplete Multi-view Clustering via Hierarchical Semantic Alignment and Cooperative Completion
Xiaojian Ding, Lin Zhao, Xian Li, Xiaoying Zhu
To address the challenges of insufficient semantic alignment across heterogeneous views, rigid fusion strategies, and the decoupling of missing view imputation from representation learning in incomplete multi-view clustering, this paper proposes HSACC, a novel framework based on Hierarchical Semantic Alignment and Cooperative Completion.HSACC introduces a dual-level semantic space design. In the low-level semantic space, consistency alignment is achieved by maximizing mutual information across views. In the high-level semantic space, adaptive view weights are dynamically assigned based on the distribution affinity between individual views and an initial fused representation. These weights are then used to perform weighted fusion, generating a unified global representation.Additionally, HSACC implicitly recovers missing views by projecting aligned latent representations into high-dimensional semantic spaces and jointly optimizes reconstruction and clustering to enable cooperative learning.Experimental results demonstrate that HSACC significantly outperforms state-of-the-art methods on five benchmark datasets. Ablation studies validate the effectiveness of the hierarchical alignment and dynamic weighting mechanisms, while parameter analysis confirms the model's robustness to hyperparameter variations.