{"authors":[{"id":null,"fullName":"Wang, Rui","name":"Rui","surname":"Wang","rank":1,"pid":null},{"id":null,"fullName":"Jiang, Yuting","name":"Yuting","surname":"Jiang","rank":2,"pid":null},{"id":null,"fullName":"Luo, Xiaoqing","name":"Xiaoqing","surname":"Luo","rank":3,"pid":null},{"id":null,"fullName":"Wu, Xiao-Jun","name":"Xiao-Jun","surname":"Wu","rank":4,"pid":null},{"id":"orcid_______::759d3ab6618d00adbdd97e20faae993c","fullName":"Sebe, Nicu","name":"Nicu","surname":"Sebe","rank":5,"pid":{"id":{"scheme":"orcid","value":"0000-0002-6597-7248"},"provenance":null}},{"id":null,"fullName":"Chen, Ziheng","name":"Ziheng","surname":"Chen","rank":6,"pid":null}],"openAccessColor":null,"publiclyFunded":false,"eoscIfGuidelines":null,"type":"publication","language":{"code":"und","label":"Undetermined"},"countries":[{"code":"IT","label":"Italy","provenance":null}],"subjects":[{"subject":{"scheme":"keyword","value":"FOS: Computer and information sciences"},"provenance":null},{"subject":{"scheme":"keyword","value":"Computer Vision and Pattern Recognition (cs.CV)"},"provenance":null},{"subject":{"scheme":"FOS","value":"0202 electrical engineering, electronic engineering, information engineering"},"provenance":null},{"subject":{"scheme":"FOS","value":"02 engineering and technology"},"provenance":null},{"subject":{"scheme":"keyword","value":"Computer Vision and Pattern Recognition"},"provenance":null}],"mainTitle":"Wasserstein-Aligned Hyperbolic Multi-View Clustering","subTitle":null,"descriptions":["<jats:p>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 (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. 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