Harnessing Causality-Driven Meta-Learning for Imputation in Incomplete Multi-view Clustering

In recent years, incomplete multi-view clustering algorithms (IMVC) have attracted extensive attention. In IMVC, the quality of missing-view imputation is a key factor determining clustering quality. However, existing IMVC approaches predominantly exploit statistical correlations and cross-view contrastive information for view completion. They fail to account for two intrinsic data characteristics: 1) the inherent causal relationships that define the logical connections between samples; 2) the superfluous features that lack discriminative power for downstream clustering tasks. This joint deficiency ultimately renders clustering predictions heavily dependent on extrinsic observed data patterns, resulting in suboptimal clustering performance. To address these issues, we propose a novel IMVC method based on Causal Meta-Learning Imputation (CMLI), which embeds causal knowledge into the imputation module orchestrated by meta-learning. Specifically, CMLI adopts meta-learning to distill critical causal information from features, thereby improving the discrimination of task-irrelevant information in the imputation process. Leveraging the Information Bottleneck (IB) principle, CMLI further optimizes the trade-off between compressing local features and preserving global features by simultaneously minimizing one mutual information term while maximizing another. Finally, the consistency learning between the label features effectively exploits cross-view clustering consistency and complementarity, resulting in a unified framework that couples clustering assignment with multi-view representation learning. Comprehensive experimental results validate that our proposed method surpasses state-of-the-art approaches.