Incomplete Multi-View Clustering via Correntropy and Complement Consensus Learning

—Incomplete multi-view clustering (IMVC) aims to leverage complementary information from multi-view data with missing instances to enhance clustering performance. Many existing IMVC methods exhibit limitations in effectively exploiting hidden information and addressing distribution differences between views and modules. To address these challenges, we present a novel IMVC framework that leverages the proposed stack feature-based matrix completion to impute the missing instances, enhancing the exploitation of underlying information. We also incorporate graph consensus to integrate graph structures learned from both completed and observed data. Additionally, we introduce correntropy-induced metric as a flexible measurement to adaptively assign different constraints to various views and modules. Furthermore, we derive an efficient iterative algorithm based on Fenchel conjugate and accelerated block coordinate update (BCU) to solve the joint learning problem. Experimental results on eight benchmark datasets demonstrate the superior performance of our method compared to state-of-the-art IMVC methods across various metrics.