Dual-Level Distribution Alignment for Deep Incomplete Multi-View Clustering

Incomplete Multi-view Clustering (IMvC) aims to perform effective clustering in the presence of missing views by exploiting the available information. While many existing approaches demonstrate satisfactory performance, their failure to adequately optimize the recovered data often limits the quality of learned representations and thus hampers clustering performance. To address this challenge, we propose a novel method, Dual-Level Distribution Alignment for Deep Incomplete Multi-View Clustering (DDAIMVC). To effectively address missing data, DDAIMVC employs a fusion-fill strategy to recover incomplete views. The recovered data from each view are then concatenated and processed through an attention mechanism to generate a unified high-level representation. To ensure consistent information across views, the framework performs distribution alignment at both the instance and cluster levels. Specifically, instance-level distribution alignment is conducted by minimizing the maximum mean discrepancy among views, while cluster-level distribution alignment is enhanced via prototypical contrastive learning, which encourages coherent cluster assignments across different modalities. Through the co-optimization of dual-level distribution alignment, the common representation reveals a clear clustering structure. Experimental results on benchmark multi-view datasets demonstrate that DDAIMVC consistently achieves state-of-the-art clustering performance.