Missing-View Self-Distillation Imputation for Incomplete Multi-View Clustering

Real-world multi-view data often contain missing views due to failures during acquisition, transmission, or storage. This makes it difficult to directly apply multi-view clustering methods that require complete samples. Existing methods typically recover missing information through cross-view prediction, neighborhood structure, or relation propagation. However, some graph-based methods rely on shared relations that may not capture specific structure of target view, potentially introducing bias into the recovered representations. To address this limitation, we propose Missing-View Self-Distillation Imputation for Incomplete Multi-View Clustering (MVSI-IMVC). Specifically, we construct a target-view self-distillation predictor. We use full-information and pseudo-missing states from complete samples and use the observed target view to provide direct supervision. Teacher predictions further guide target-view latent recovery for incomplete samples. This allows the model to learn predictive relationships specific to each target view and missing pattern. We also introduce Henze–Penrose divergence-guided fused representation learning. Together with a cross-view instance contrastive loss, it organizes completed latent representations at the instance and distribution levels and connects target-view latent recovery with fused representation learning and clustering. Experiments show that MVSI-IMVC achieves competitive clustering performance on multiple benchmark datasets.