标题： 通过分层语义对齐和协同补全的不完整多视图聚类 显示英文标题

标题： Incomplete Multi-view Clustering via Hierarchical Semantic Alignment and Cooperative Completion

摘要： 不完整的多视图数据，其中某些样本的某些视图完全缺失，给传统多视图聚类方法带来了重大挑战。 现有的深度不完整多视图聚类方法通常依赖于静态融合策略或两阶段流程，导致融合结果不理想和误差传播问题。 为解决这些限制，本文提出了一种基于分层语义对齐与协同补全（HSACC）的新颖不完整多视图聚类框架。 HSACC通过双层次语义空间设计实现鲁棒的跨视图融合。 在低层次语义空间中，通过最大化不同视图之间的互信息来确保一致性对齐。 在高层次语义空间中，根据单个视图与初始融合表示之间的分布相似性动态分配自适应视图权重，随后进行加权融合以生成统一的全局表示。 此外，HSACC通过将对齐的潜在表示投影到高维语义空间来隐式恢复缺失视图，并联合优化重建和聚类目标，从而实现补全和聚类的协同学习。 实验结果表明，HSACC在五个基准数据集上显著优于现有最先进方法。 消融研究验证了分层对齐和动态加权机制的有效性，而参数分析证实了模型对超参数变化的鲁棒性。 显示英文摘要

摘要： Incomplete multi-view data, where certain views are entirely missing for some samples, poses significant challenges for traditional multi-view clustering methods. Existing deep incomplete multi-view clustering approaches often rely on static fusion strategies or two-stage pipelines, leading to suboptimal fusion results and error propagation issues. To address these limitations, this paper proposes a novel incomplete multi-view clustering framework based on Hierarchical Semantic Alignment and Cooperative Completion (HSACC). HSACC achieves robust cross-view fusion through a dual-level semantic space design. In the low-level semantic space, consistency alignment is ensured by maximizing mutual information across views. In the high-level semantic space, adaptive view weights are dynamically assigned based on the distributional affinity between individual views and an initial fused representation, followed by weighted fusion to generate 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 objectives, enabling cooperative learning of completion and clustering. 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.