Deep Incomplete Multi-View Clustering via Dynamic Imputation and Triple Alignment With Dual Optimization

In recent years, Incomplete Multi-View Clustering (IMVC) has become an important and challenging task. Although several methods have been proposed to address IMVC, they still have the following drawbacks: i) Due to the presence of missing samples in the views, clustering prototypes obtained from different views may have positional deviations, leading to inaccurate positioning of cluster centers, thus affecting the accuracy of clustering results. ii) Repair strategies based on cross-view prediction and adversarial generation have high computational costs and heavily rely on model performance. Neighbor-based repair strategies may result in inaccurate neighbor selection due to the presence of noise. iii) Models learned solely from complete data often perform better than models learned from both complete and incomplete data, especially when there are semantic differences between the repaired data and the missing data. To address the aforementioned issues, this paper proposes a Dynamic Imputation and Triple Alignment with Dual-Optimization for Deep Incomplete Multi-View Clustering (DITA-IMVC). Specifically, by accurately representing advanced features, we propose a cross-view dynamic structure learning strategy for missing view repair, where the dynamic structural relationships between high-semantic features within each view are calculated to obtain highly related samples from different views. To address positional deviations from different views, we propose a triple cross-view alignment with prototype, feature, and clustering assignment, which preserves the consistency among different views. Finally, we design a dual-optimization process for both complete view features and repaired features via alternating iterations to fully utilize the incomplete view data, thereby improving clustering performance. To demonstrate the effectiveness of our DITA-IMVC, extensive experiments conducted on different standard datasets show that it yields superior clustering results compared to existing methods.