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arXiv · 2607.20084

Non--negative matrix factorization using the \textit{R} package \textsf{nnmf}

Volkan Sevinç·Nikolas Kontemeniotis·Theodoros Perdikis·Michail Tsagris
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Abstract

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.

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Volkan Sevinç, Nikolas Kontemeniotis, Theodoros Perdikis, Michail Tsagris. 2026-07-22. Non--negative matrix factorization using the \textit{R} package \textsf{nnmf}. https://arxiv.org/abs/2607.20084

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