Non-negative Matrix Factorization: A Survey. (19th July 2021)

• Record Type: Journal Article
• Authors: Gan, Jiangzhang; Liu, Tong; Li, Li; Zhang, Jilian
• Is Part Of: Computer journal. Volume 64:Number 7(2021)
• Page Start: 1080
• Page End: 1092
• Publication Date: 2021-07-19
• Subjects: non-negative matrix factorization -- data mining -- dimensionality reduction -- clustering; Computers -- Periodicals; 005.1
• DOI: 10.1093/comjnl/bxab103
• Languages: English
• ISSNs: 0010-4620

Abstract

Abstract: Non-negative matrix factorization (NMF) is a powerful tool for data science researchers, and it has been successfully applied to data mining and machine learning community, due to its advantages such as simple form, good interpretability and less storage space. In this paper, we give a detailed survey on existing NMF methods, including a comprehensive analysis of their design principles, characteristics and drawbacks. In addition, we also discuss various variants of NMF methods and analyse properties and applications of these variants. Finally, we evaluate the performance of nine NMF methods through numerical experiments, and the results show that NMF methods perform well in clustering tasks.