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.

中文翻译：

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非负矩阵分解：调查

非负矩阵分解（NMF）是数据科学研究人员的有力工具，由于其形式简单、可解释性好、存储空间少等优点，已成功应用于数据挖掘和机器学习社区。在本文中，我们对现有的 NMF 方法进行了详细的调查，包括对其设计原理、特点和缺点的综合分析。此外，我们还讨论了 NMF 方法的各种变体，并分析了这些变体的性质和应用。最后，我们通过数值实验评估了九种 NMF 方法的性能，结果表明 NMF 方法在聚类任务中表现良好。