Diagnosing and Preventing MSE-Alignment-Induced Representation Collapse in Unaligned Multi-View Clustering

Fully unaligned multi-view clustering aims to recover unknown sample correspondences across views and uncover a shared cluster structure from unaligned representations. A common strategy is to construct learnable cross-view alignment matrices and optimize them using the mean squared error (MSE) loss for sample alignment and cross-view fusion. However, our empirical observations reveal that directly optimizing the alignment matrices and latent representations under the MSE objective can lead to a scale-shrinking shortcut, termed MSE-alignment-induced representation collapse, which compresses inter-sample distances and distorts the underlying cluster structure. To address this issue, we first theoretically analyze the mechanism underlying this collapse and then propose Representation Dispersion-Enhanced Unaligned Multi-view Clustering (RDE-UMC), which effectively mitigates MSE-alignment-induced representation collapse by enhancing the dispersion of latent representations while preserving discriminative cluster structures. Specifically, guided by the analyzed collapse mechanism, we first design a representation dispersion enhancement operator that quantifies dispersion across principal directions to mitigate representation collapse while maintaining the separability of representations in each view. We then incorporate this operator into the MSE objective to formulate Representation Dispersion-Enhanced MSE (RDE-MSE) to perform cross-view alignment on separable representations. Building on the aligned representations, we further integrate the operator into contrastive learning to formulate Representation Dispersion-Enhanced Contrastive Learning (RDE-CL), which further facilitates cross-view consistency fusion among the aligned representations. Experiments on benchmark datasets demonstrate that RDE-UMC effectively mitigates MSE-alignment-induced representation collapse, preserves representation dispersion and discriminability, and improves clustering performance.