DUIMC: Deep Unbalanced Incomplete Multi-View Clustering via Graph Constrained Imputation and Contrastive Learning

Due to the frequent occurrence of missing views in real-world multi-view data, incomplete multi-view clustering (IMVC) has attracted significant attention. However, most existing IMVC methods overlook the fact that incomplete data in practical applications often exhibits varying missing rates across different views, rendering their mechanisms ineffective under such conditions. Although several works based on conventional learning methods have been proposed to solve unbalanced incomplete multi-view clustering (UIMVC), their performance is limited by their shallow feature representation and over-sophisticated optimization procedure. In this paper, we propose Deep Unbalanced Incomplete Multi-view Clustering via Graph Constrained Imputation and Contrastive Learning (DUIMC) to address UIMVC with deep learning paradigm. Specifically, DUIMC introduces a novel differentiable imputation layer for dynamically handling unbalanced incompleteness and integrates it with multi-view contrastive clustering into a unified deep representation learning framework. Furthermore, bi-level graph constraints are imposed on imputation and representation learning to preserve local consistency at both the feature and instance levels. In addition, we develop adaptive fusion mechanisms to adaptively restrain the impact aroused by information unbalance among views. Extensive experimental results on five benchmark datasets demonstrate DUIMC's superior clustering performance over several traditional state-of-the-art approaches.