AEMVC: Mitigate Imbalanced Embedding Space in Multi-view Clustering

Pengyuan Li, Man Liu, Dongxia Chang, Yiming Wang, Zisen Kong, Yao Zhao

Abstract: Multi-view clustering (MVC) has gained extensive attention for its capacity to handle heterogeneous data. However, current autoencoder-based MVC methods suffer from a limitation: embedding space exhibits severe imbalances in the efficacy of feature direction, creating a long-tailed singular value distribution where few directions dominate. To mitigate this, we introduce a novel Activate-Then-Eliminate Strategy for Multi-View Clustering (AEMVC), inspired by the observation that balanced feature directions can facilitate enhancing discrimination of learned representations. AEMVC dynamically adjusts the contributions of different feature directions through two keys: a Feature Activation Module that narrows singular value discrepancies to prevent dominant directions from controlling clustering decisions, and an Inter-view Mutual Supervision strategy that filters redundant information by adaptively determining view-specific thresholds based on cross-view consistency. By activating more feature directions and eliminating each view's adverse factors, AEMVC achieves more balanced and discriminative embedding representations. Extensive experiments on seven multi-view benchmarks validate AEMVC's effectiveness, demonstrating substantial improvements over state-of-the-art methods.