Hyperbolic Enhanced Representation Learning for Incomplete Multi-view Clustering

TL;DR

This paper introduces HERL, a hyperbolic representation learning framework for incomplete multi-view clustering that models hierarchical data structures more effectively than Euclidean methods.

Contribution

HERL employs a hyperbolic space with dual-constraint contrastive mechanisms and prototype alignment to improve clustering with incomplete multi-view data.

Findings

HERL outperforms existing methods on multiple datasets.

The hyperbolic contrastive mechanism preserves semantic identity.

Hierarchical modeling reduces semantic blurring in representations.

Abstract

Incomplete Multi-View Clustering (IMVC) faces the challenge of learning discriminative representations from fragmentary observations while maintaining robustness against missing views. However, prevalent Euclidean-based methods suffer from a geometric mismatch when modeling real-world data with intrinsic hierarchies, leading to semantic blurring where representations drift towards spatially proximal but semantically distinct neighbors. To bridge this gap, we propose HERL, a Hyperbolic Enhanced Representation Learning framework for IMVC. Operating within the Poincar\'e ball, HERL constructs a structure-aware latent space to enhance representation learning. Specifically, we design a dual-constraint hyperbolic contrastive mechanism optimizing: an angular-based loss to preserve semantic identity via directional alignment, and a distance-based loss to enforce hierarchical compactness. Furthermore, a hyperbolic prototype head is introduced to rectify global structural drift by aligning cross-view hierarchy-aware prototype distributions. Consequently, HERL disentangles fine-grained semantic correlations to sharpen cluster boundaries and imposes geometric constraints to rectify the data recovery process. Extensive experimental results demonstrate that HERL consistently outperforms state-of-the-art approaches.