# Wasserstein-Aligned Hyperbolic Multi-View Clustering

arXiv: https://arxiv.org/abs/2512.09402 · HTML: https://mlsanity.com/paper/2512.09402/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 41 references that are in the map.
- First posted on arXiv 10 Dec 2025.

## Builds on (most cited first, 16 of 16)

- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic Geometry](https://mlsanity.com/paper/1806.03417/) (2018)
- [Low-Dimensional Hyperbolic Knowledge Graph Embeddings](https://mlsanity.com/paper/2005.00545/) (2020)
- [Fully Hyperbolic Neural Networks](https://mlsanity.com/paper/2105.14686/) (2021)
- [Twin Contrastive Learning for Online Clustering](https://mlsanity.com/paper/2210.11680/) (2022)
- [Multi-VAE: Learning Disentangled View-common and View-peculiar Visual Representations for Multi-view Clustering](https://mlsanity.com/paper/2106.11232/) (2021)
- [Deep Multiview Clustering by Contrasting Cluster Assignments](https://mlsanity.com/paper/2304.10769/) (2023)
- [Deep Incomplete Multi-view Clustering with Cross-view Partial Sample and Prototype Alignment](https://mlsanity.com/paper/2303.15689/) (2023)
- [Stability Analysis and Generalization Bounds of Adversarial Training](https://mlsanity.com/paper/2210.00960/) (2022)
- [XAI Beyond Classification: Interpretable Neural Clustering](https://mlsanity.com/paper/1808.07292/) (2018)
- [One-Pass Incomplete Multi-view Clustering](https://mlsanity.com/paper/1903.00637/) (2019)
- [Contrastive Multi-view Hyperbolic Hierarchical Clustering](https://mlsanity.com/paper/2205.02618/) (2022)
- [Investigating and Mitigating the Side Effects of Noisy Views for Self-Supervised Clustering Algorithms in Practical Multi-View Scenarios](https://mlsanity.com/paper/2303.17245/) (2023)
- [Deep Multi-View Subspace Clustering with Anchor Graph](https://mlsanity.com/paper/2305.06939/) (2023)
- [Incomplete Multi-view Clustering via Diffusion Contrastive Generation](https://mlsanity.com/paper/2503.09185/) (2025)
- [Intrinsic Sliced Wasserstein Distances for Comparing Collections of Probability Distributions on Manifolds and Graphs](https://mlsanity.com/paper/2010.15285/) (2020)

## Built on by (most cited first, 3 of 3)

- [Hyperbolic Busemann Neural Networks](https://mlsanity.com/paper/2602.18858/) (2026)
- [Semi-Supervised Hyperbolic Hierarchical Clustering with Set-Level Structural Priors](https://mlsanity.com/paper/2606.01525/) (2026)
- [Pretraining Reusable Inference Across Views with Synthetic Task Priors](https://mlsanity.com/paper/2608.19115/) (2026)

## Abstract (arXiv)

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 (textite.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.