用双曲空间对齐多视图数据，提升聚类精度与鲁棒性。
Wasserstein-Aligned Hyperbolic Multi-View Clustering
- 各视图独立映射到双曲空间，建模层次化语义结构。
- 引入切片沃瑟斯坦距离，对齐跨视图流形分布，降低噪声干扰。
- 适合处理具有层次结构的多视图数据，如图像、文本融合任务。
多视图聚类旨在通过学习视图共性和特异性信息来揭示多视图数据的潜在结构。尽管近期研究已探索使用双曲表示以缓解不同视图间的表征差异，但主要聚焦于实例级对齐，忽视了全局语义一致性，因而易受视图特异性信息（如噪声和跨视图偏差）影响。为此，本文提出一种新颖的Wasserstein对齐双曲（WAH）框架用于多视图聚类。具体而言，方法为每个视图设计视图特异性双曲编码器，将特征嵌入Lorentz流形以实现层次化语义建模；随后引入基于双曲切片沃瑟斯坦距离的全局语义损失，对齐跨视图流形分布；最后通过软聚类分配增强跨视图语义一致性。在多个基准数据集上的大量实验表明，该方法可达到当前最优（SOTA）聚类性能。
原文摘要 · Abstract (English)
Multi-view clustering (MVC) aims to uncover the latent structure of multi-view data by learning view-common and view-specific information. Although recent studies have explored hyperbolic representations for better tackling the representation gap between different views, they focus primarily on instance-level alignment and neglect global semantic consistency, rendering them vulnerable to view-specific information (\textit{e.g.}, noise and cross-view discrepancies). To this end, this paper proposes a novel Wasserstein-Aligned Hyperbolic (WAH) framework for multi-view clustering. Specifically, our method exploits a view-specific hyperbolic encoder for each view to embed features into the Lorentz manifold for hierarchical semantic modeling. Whereafter, a global semantic loss based on the hyperbolic sliced-Wasserstein distance is introduced to align manifold distributions across views. This is followed by soft cluster assignments to encourage cross-view semantic consistency. Extensive experiments on multiple benchmarking datasets show that our method can achieve SOTA clustering performance.
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