Wasserstein-Aligned Hyperbolic Multi-View Clustering

2025/12/10 by Rui Wang, Wang, Rui, Yuting Jiang +9
Computer Science · #Face recognition and analysis #Domain Adaptation and Few-Shot Learning #Face and Expression Recognition

paper · doi:10.48550/arxiv.2512.09402

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 (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.

Citations

• Incomplete Multi-view Clustering via Diffusion Contrastive Generation
• Deep Multi-View Subspace Clustering with Anchor Graph
• Investigating and Mitigating the Side Effects of Noisy Views for Self-Supervised Clustering Algorithms in Practical Multi-View Scenarios
• Deep Incomplete Multi-view Clustering with Cross-view Partial Sample and Prototype Alignment
• Stability Analysis and Generalization Bounds of Adversarial Training
• Contrastive Multi-view Hyperbolic Hierarchical Clustering
• Multi-VAE: Learning Disentangled View-common and View-peculiar Visual Representations for Multi-view Clustering
• Fully Hyperbolic Neural Networks
• Intrinsic Sliced Wasserstein Distances for Comparing Collections of Probability Distributions on Manifolds and Graphs
• Low-Dimensional Hyperbolic Knowledge Graph Embeddings
• One-Pass Incomplete Multi-view Clustering
• Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic Geometry
• Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Related

• Contrastive Multi-view Hyperbolic Hierarchical Clustering
• Multi-view Hierarchical Clustering
• Incomplete Multi-view Clustering via Hierarchical Semantic Alignment and Cooperative Completion
• High-dimensional multi-view clustering methods
• A Self-Organizing Tensor Architecture for Multi-View Clustering