非负矩阵分解
可解释性
计算机科学
矩阵分解
聚类分析
机器学习
数据挖掘
简单(哲学)
人工智能
因式分解
基质(化学分析)
算法
量子力学
认识论
物理
哲学
特征向量
复合材料
材料科学
作者
Jiangzhang Gan,Tong Liu,Li Li,Jilian Zhang
标识
DOI:10.1093/comjnl/bxab103
摘要
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.
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