Multi-view clustering based on pairwise co-regularization and robust dual graph non-negative matrix factorization

Abstract

Multi-view clustering has been a hotspot in the field of machine learning and pattern recognition, and methods based on non-negative matrix factorization have gained attention for their simplicity and interpretability. Despite these methods achieving great clustering performance, there may also be some limitations, such as the full structural information of data and the similarity between different views not being considered. This paper proposes a novel multi-view clustering algorithm based on pairwise co-regularization and robust dual graph non-negative matrix factorization. Firstly, the l2,p-norm is applied in non-negative matrix factorization framework to enhance model robustness. Next, pairwise co-regularization is utilized to extract the inter-view information of all views. Then, graph dual regularization is applied to preserve the structure information of the data and feature spaces. Lastly, an auto-weighted strategy is introduced to assign appropriate weights to each view. In addition, an iterative updating optimization scheme for the proposed algorithm is developed, and the convergence proof of the scheme is provided. The experimental results on twelve real-world datasets show that the proposed algorithm is both effective and efficient, compared with three classical and six state-of-the-art algorithms.