Wasserstein-Aligned Hyperbolic Multi-View Clustering
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914191629090816 |
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| author | Wang, Rui Jiang, Yuting Luo, Xiaoqing Wu, Xiao-Jun Sebe, Nicu Chen, Ziheng |
| author_facet | Wang, Rui Jiang, Yuting Luo, Xiaoqing Wu, Xiao-Jun Sebe, Nicu Chen, Ziheng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_09402 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Wasserstein-Aligned Hyperbolic Multi-View Clustering Wang, Rui Jiang, Yuting Luo, Xiaoqing Wu, Xiao-Jun Sebe, Nicu Chen, Ziheng Computer Vision and Pattern Recognition 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. |
| title | Wasserstein-Aligned Hyperbolic Multi-View Clustering |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.09402 |