Non-negative Matrix Factorization: A Survey
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Date
2021-07-01
Open Access Location
Journal Title
Journal ISSN
Volume Title
Publisher
Oxford University Press on behalf of the British Computer Society
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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.
Description
CAUL read and publish agreement 2022
Keywords
non-negative matrix factorization, data mining, dimensionality reduction, clustering
Citation
COMPUTER JOURNAL, 2021, 64 (7), pp. 1080 - 1092