Relative Semantic Representation Learning for Partially View-Aligned Clustering

Partially view-aligned clustering (PVC) aims to cluster multi-view data with partially known cross-view correspondences. However, existing PVC methods still face two limitations: i) view-specific representations may not sufficiently capture the relational semantics required for reliable cross-view correspondence estimation; and ii) a uniform association strategy may underutilize known correspondences and yield unreliable associations for unaligned samples. To address these issues, we propose Relative Semantic Representation Learning for Partially View-Aligned Clustering (RSR-PVC). Specifically, each sample is represented by a normalized distribution of its relations to the cross-view aligned samples, forming a relative semantic representation for cross-view comparison. Subsequently, a hard-to-soft relation-space association mechanism is developed to handle aligned and unaligned samples differently. Known aligned correspondences guide bidirectional matching, while associations for unaligned samples are modeled with soft distributions. Finally, the resulting associations are incorporated into relational consistency learning to enhance cross-view representation consistency. Extensive experiments on eight benchmark datasets against seven state-of-the-art methods demonstrate the effectiveness of RSR-PVC.