Wasserstein-Aligned Hyperbolic Multi-View Clustering

TL;DR

This paper introduces a Wasserstein-Aligned Hyperbolic framework for multi-view clustering that leverages hyperbolic embeddings and Wasserstein distance to improve global semantic consistency and achieve state-of-the-art results.

Contribution

It proposes a novel hyperbolic multi-view clustering method using Wasserstein alignment to enhance semantic consistency across views.

Findings

Achieves state-of-the-art clustering performance on benchmark datasets.

Effectively aligns view-specific distributions in hyperbolic space.

Improves robustness to view-specific noise and discrepancies.

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.