Re-weighted multi-view clustering via triplex regularized non-negative matrix factorization

• Published in: Neurocomputing (Amsterdam) Vol. 464; pp. 352 - 363
• Main Authors: Feng, Lin, Liu, Wenzhe, Meng, Xiangzhu, Zhang, Yong
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 13.11.2021
• Subjects: Multi-view clustering; Non-negative matrix factorization; Regularized; Regularized; Non-negative matrix factorization; Multi-view clustering
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2021.08.113

Multi-view clustering, which aims at dividing data with similar structures into their respective groups, is a popular research subject in computer vision and machine learning. In recent years, Non-negative matrix factorization (NMF) has received constant concern in multi-view clustering due to its ability to deal with high-dimensional data. However, most existing NMF methods may fail to integrate valuable information from multi-view data adequately, and the local geometry structure in data is also not fully considered. Thus, it’s still a crucial but challenging problem, which effectively extracts multi-view information while maintaining the low-dimensional geometry structure. In this paper, we propose an innovative multi-view clustering method, referred to as re-weighted multi-view clustering via triplex regularized non-negative matrix factorization (SMCTN), which is a unified framework and provides the following contributions: 1) pairwise regularization can extract complementary information between views and is suitable for both homogeneous and heterogeneous perspectives; 2) consensus regularization can process the consistent information between views; 3) graph regularization can preserve the geometric structure of data. Specifically, SMCTN applies a re-weighted strategy to assign suitable weights for multiple views according to their contributions. Besides, an effective iterative updating algorithm is developed to solve the non-convex optimization problem in SMCTN. Extensive experimental results on textual and image datasets indicate that the superior performance of the proposed method.