Non-negative Matrix Factorization: A Survey

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 deta...

Full description

Saved in:
Bibliographic Details
Published inComputer journal Vol. 64; no. 7; pp. 1080 - 1092
Main Authors Gan, Jiangzhang, Liu, Tong, Li, Li, Zhang, Jilian
Format Journal Article
LanguageEnglish
Published Oxford University Press 01.07.2021
Subjects
dimensionality reduction
clustering
non-negative matrix factorization
data mining
Online AccessGet full text

Cover

More Information
Summary: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.
ISSN:0010-4620
1460-2067
DOI:10.1093/comjnl/bxab103