Decoupled Conditional Alignment for Partially View-Aligned Clustering

Partially View-aligned Clustering (PVC) aims to handle complex multi-view data where only a small subset of samples exhibits strict cross-view correspondence. However, existing methods often overlook intra-view geometric topology during the feature extraction process. Furthermore, blindly aligning massive unpaired samples risks false-negative repulsion, leading to severe semantic confusion and feature manifold collapse. To address these critical issues, we propose a divide-and-conquer framework termed Decoupled Conditional Alignment (DeCA). DeCA first employs a dual-decoder Graph Autoencoder (GAE) to preserve the native intra-view neighborhood connectivity for extracting structure-preserved latent representations. At the sample level, it constructs stable global semantic anchors via strong cross-view contrast on aligned samples. For unaligned samples, pseudo-label-guided conditional Maximum Mean Discrepancy (MMD) and intra-view neighborhood contrast ensure intra-class sub-distribution matching without cross-view pairings. Finally, a decoupled feature fusion strategy is introduced to mitigate manifold contamination caused by superimposing heterogeneous misaligned features. Extensive experiments on ten benchmark datasets demonstrate DeCA's superiority over various PVC baselines.