Dual-dimensional contrastive learning for incomplete multi-view clustering

Abstract

Incomplete multi-view clustering (IMVC) is a critical task in real-world applications, where missing data in some views can severely limit the ability to leverage complementary information across views. This issue leads to incomplete sample representations, hindering model performance. Current contrastive learning methods for IMVC exacerbate the problem by directly constructing data pairs from incomplete samples, ignoring essential information and resulting in class collisions, where samples from different classes are incorrectly grouped together due to a lack of label guidance. These challenges are particularly detrimental in fields like recommendation systems and bioinformatics, where accurate clustering of high-dimensional and incomplete data is essential for decision-making. To address these issues, we propose Dual-dimensional Contrastive Learning (DCL), an online IMVC model that fills missing values through multi-view consistency transfer, enabling simultaneous clustering and representation learning via instance-level and cluster-level contrastive learning in both row and column spaces. DCL mitigates class collision issues by generating high-confidence pseudo-labels and using an optimal transport matrix, significantly improving clustering accuracy. Extensive experiments demonstrate that DCL achieves state-of-the-art results across five datasets. The code is available at https://github.com/2251821381/DCL.