Reliable Feature Imputation With Cross-View Relation Transfer for Deep Incomplete Multi-View Classification

Incomplete Multi-view Classification has sparked widespread interest in recent years, since multi-view data suffering from missing values are ubiquitous in real-world scenarios. While many imputation-based methods recover missing data by exploiting inter-sample structural information within individual views, they are inherently susceptible to unreliable or noisy samples, which can lead to low-quality imputation and degrade classification accuracy. Therefore, it is a challenge to effectively mine the multi-stage complex correlations for incomplete multi-view data to achieve reliable imputation and obtain discriminative representation. To address these issues, we present a novel imputation-based approach called Reliable Feature Imputation with Cross-view Relation Transfer for Deep Incomplete Multiview Classification (RFI-IMvC). Our framework fully exploits inter-view and intra-view structural information in multi-stage manner. Specifically, we propose a novel cross-view relation transfer strategy to recover reliable neighbor relationships and achieve high-quality imputation for missing data. Besides, to fully exploit the structural information in reconstructed multi-view data, we develop a dual graph learning module to mine high-order semantic correlation and facilitate interactions of complementary information from instances linked by hyperedge. Finally, inspired by prototype learning, we incorporate a class--level representation loss to further promote intra-class compactness. Extensive experiments on 7 real-world datasets demonstrate that our method outperforms state-of-the-art methods.