PGFormer: A Prototype-Graph Transformer for Incomplete Multiview Clustering

Incomplete multiview clustering (IMVC) faces significant challenges due to missing data and inherent view discrepancies. While deep neural networks offer powerful representation learning capabilities for IMVC, existing methods often overlook view diversity and force representations across views to be identical, leading to 1) biased representations with distorted topologies and 2) inaccurate imputation for missing data, ultimately degrading clustering performance. To address these issues, we propose prototype-graph transformer (PGFormer), a novel IMVC framework that integrates prototype assignments, rather than direct representations, to enhance clustering performance. PGFormer leverages view-specific encoders to extract features from available samples in each view, employs a PGFormer designed to refine node embeddings, and reconstructs available samples using these refined embeddings. For each view, PGFormer utilizes a graph convolutional network (GCN) to model node-to-node topologies and generate semantic prototypes from the node embeddings. These view-specific prototypes and embeddings are then refined through dual attention mechanisms: prototype-to-prototype (P2P) self-attention and prototype-to-node (P2N) cross-attention, enabling a thorough exploration of multilevel topological relationships within each view. To address missing data, the cross-prototype imputation (CPI) module leverages the weighted prototype assignments from different views to impute missing samples using refined intraview prototypes. Building on this, the cross-view alignment module calibrates prototype assignments to ensure consistent predictions across views. Extensive experiments demonstrate that PGFormer can achieve superior performance compared with the baselines.