A Unified Framework for Pseudo-Supervised Clustering via Weighted Sample Aggregation

Multi-view clustering methods based on graph learning have attracted considerable attention due to their superior clustering performance. However, such approaches generally lack effective supervisory signals, making it difficult to fully exploit the latent correlations among multi-view data. In addition, existing methods often treat all samples equally in local learning, which limits their ability to capture critical local structures. To address these issues, A Unified Framework for Pseudo-Supervised Clustering via Weighted Sample Aggregation (PSC-WSA) is proposed, which constructs a complete pseudo supervision-guided clustering framework encompassing pseudo supervision information generation, weighted sample aggregation, clustering, and label propagation. In this framework, the sample aggregation process is learnable under the constraints of pseudo-supervision, and synergistically interacts with the clustering process, serving as the key bridge for pseudo-supervision guided clustering. Moreover, two weighted aggregation strategies are designed to adaptively model local relationships between highly similar samples. This enables diverse sample-level locality learning across different views, thereby effectively enhancing the discriminability of the representative samples. To optimize the PSC-WSA model, an efficient alternating iterative optimization algorithm is developed. Extensive experiments on both multi view datasets and multimodal remote sensing datasets validate the feasibility, effectiveness and strong scalability.