Wasserstein-Aligned Hyperbolic Multi-View Clustering

• URL: http://arxiv.org/abs/2512.09402v1
• Date: Wed, 10 Dec 2025 07:56:19 GMT
• Title: Wasserstein-Aligned Hyperbolic Multi-View Clustering
• Authors: Rui Wang, Yuting Jiang, Xiaoqing Luo, Xiao-Jun Wu, Nicu Sebe, Ziheng Chen,
• Abstract summary: This paper proposes a novel Wasserstein-Aligned Hyperbolic (WAH) framework for multi-view clustering.<n>Our method exploits a view-specific hyperbolic encoder for each view to embed features into the Lorentz manifold for hierarchical semantic modeling.
• Score: 58.29261653100388
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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