Incomplete multi-view subspace clustering based on robust matrix completion

Multi-view clustering (MVC) is a popular approach used in data mining and pattern recognition to enhance clustering performance by leveraging complementary information from different data sources or features. However, real-world multi-view data often suffer from incomplete views, where some samples lack representation on one or more views due to various faults during data collection or processing. Efficiently exploiting the connections between multiple views from such incomplete data presents a significant challenge compared to complete-view clustering. Although several methods have been proposed in recent years to address the problem of incomplete multi-view clustering (IMVC), existing approaches have not fully considered: (1) the differential negative effects associated with varying missing data rates across different views, and (2) the impact of intrinsic noise arising from the diverse quality of these views. To address this problem, we propose a novel subspace clustering framework based on robust matrix completion for incomplete and unlabeled multi-view data. The proposed model consists of two modules. The first is a regularized missing data completion module from an information theoretic perspective, and the second is a centralized subspace model, combining low rank properties. In addition, we develop an efficient iterative algorithm using half-quadratic (HQ) and linearized alternating direction method (ADM). Finally, we conduct experiments on five benchmark multi-view datasets to demonstrate that our proposed method outperforms state-of-the-art methods in most datasets and metrics.