使用R包nnmf进行非负矩阵分解

中文摘要 AI 辅助

非负矩阵分解（NMF）已成为从非负数据中提取潜在结构的成熟降维技术，在生物信息学、文本挖掘、图像分析和推荐系统等领域广泛应用。随着NMF的普及，众多实现不同优化策略和计算框架的R包被开发。但在实际数据条件下对这些实现的全面评估有限。本研究引入新的NMF R包，并与两个广泛使用的R包进行系统性能比较。评估使用真实数据，通过一致实验框架，重点评估计算效率、收敛行为、重构精度、内存利用率和矩阵分解稳定性。

英文摘要

Non--negative matrix factorization (NMF) has become an established dimensionality reduction technique for extracting latent structures from non--negative data and has found widespread applications in fields such as bioinformatics, text mining, image analysis, and recommender systems. As the popularity of NMF has increased, numerous \textit{R} packages implementing different optimization strategies and computational frameworks have been developed. Despite their widespread availability, comprehensive evaluations of these implementations under real--world data conditions remain limited. Consequently, researchers often lack objective guidance when selecting an appropriate package for practical applications. This study introduces a new \textit{R} package for NMF and offers asystematic performance comparison with two widely available \textit{R} packages for NMF analysis. Rather than relying on simulated datasets, the evaluation is conducted using real--world data to better reflect the complexity, heterogeneity, and noise characteristics encountered in practical analytical settings. The packages are assessed using a consistent experimental framework, with emphasis on computational efficiency, convergence behavior, reconstruction accuracy, memory utilization, and the stability of the resulting matrix factorization.

发表机构

• Muğla SıtkıKoçman University Faculty of Science Department of Statistics（穆尔加大学科学学院统计学系）
• University of Crete（克里特大学）
• University of Piraeus（比雷埃克斯大学）

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