Adaptive structural-guided multi-level representation learning with graph contrastive for incomplete multi-view clustering

Abstract

Incomplete multi-view clustering (IMC) is a pivotal task within the area of machine learning, encompassing several unresolved challenges, such as representation of objects, relations of various views, discriminative of features, and data restoration. To address these challenges, we propose a novel Adaptive Structural-guided Multi-level representation Learning with Graph Contrastive algorithm for IMC (ASMLGC), where feature learning, data restoration and clustering are simultaneously integrated. Concretely, ASMLGC learns multi-level representations of objects by extending auto-encoders, which explicitly captures hierarchical structure of multi-view data, providing a better and more comprehensive strategy to characterize data from multiple resolutions. And, missing views are recovered by leveraging multi-level representations, where global and local information are fully exploited to enhance the accuracy and robustness of imputation. Furthermore, ASMLGC proposes graph contrastive learning to maximize intra-cluster consistency, where information derived from various resolutions, such as feature level and meta-structure level, is explored to construct positive and negative samples, thereby improving discriminative of features. The extensive experimental results confirm that ASMLGC outperforms baselines on benchmarking datasets, particularly for these datasets with complicated hierarchical structure. The proposed algorithm can be applied to bioinformatics, medical image analysis, and social network analysis.