Robust Multi-view Clustering via Pseudo Label Guided Universum Learning

Recently, contrastive learning has emerged as a promising approach for multi-view clustering (MVC), as it enforces cross-view consistency and leverages complementary information from different views to enhance the analysis of heterogeneous data. However, traditional contrastive MVC methods suffer from an inherent limitation: their one-to-many contrast mechanism induces the False Negative Problem (FNP), where semantically similar intra-class instances are erroneously repelled. This phenomenon compromises intra-class consistency and ultimately degrades clustering performance. To overcome this issue, we propose a novel Pseudo lA bel gU ided univerS um lE arning (PAUSE) framework for robust multi-view clustering. Specifically, PAUSE operates in two synergistic stages: (1) A warm-up stage that employs dual contrastive learning to generate reliable pseudo-labels, establishing robust semantic relationships; (2) A fine-tuning stage that synthesizes universum samples via Mixup between anchor instances and out-of-class centroids, guided by the acquired pseudo-labels. This unique mechanism constructs generalized negative classes that expand inter-class margins while preserving intra-class cohesion. Crucially, the widened decision boundaries prevent misclassification of displaced intra-class instances, effectively circumventing FNP without requiring explicit negative pair correction. We further devise a robust universum contrastive loss that explicitly enforces cross-view consistency through adaptive boundary constraints. Extensive experiments on five multi-view benchmarks demonstrate that our PAUSE consistently outperforms 11 state-of-the-art multi-view learning methods. Our code is accessible at: https://github.com/xixi-555/PAUSE_main_code.