Non--negative matrix factorization using the extit{R} package extsf{nnmf}

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

Source

Source name: arXiv
Source kind: paper
Authors / organization: Volkan Sevinç, Nikolas Kontemeniotis, Theodoros Perdikis, Michail Tsagris
Venue: arXiv: stat.ML
Published: 2026-07-22
Collected: 2026-07-23
URL: http://arxiv.org/abs/2607.20084v1
PDF: https://arxiv.org/pdf/2607.20084v1
DOI: UNKNOWN

Plain-Language Brief

The paper introduces a new R package for Non-negative Matrix Factorization (NMF) and compares its performance with two other widely used NMF packages in R. Instead of using simulated data, the comparison is based on real-world data to better reflect practical analytical settings.

What the Document Says

Non-negative matrix factorization (NMF) is a dimensionality reduction technique with applications in bioinformatics, text mining, image analysis, and recommender systems.
There are numerous R packages for NMF, but comprehensive evaluations under real-world conditions are limited.
The paper introduces a new R package for NMF.
The new package is compared with two other widely available R packages for NMF analysis.
The comparison is based on real-world data to better reflect practical analytical settings.
The packages are assessed using a consistent experimental framework.
Assessment focuses on computational efficiency, convergence behavior, reconstruction accuracy, memory utilization, and the stability of the resulting matrix factorization.

Key Concepts Mentioned

Non-negative Matrix Factorization
R package
Dimensionality reduction
Bioinformatics
Text mining
Image analysis
Recommender systems

Claims Made by the Document

Comprehensive evaluations of NMF R packages under real-world conditions are limited.
The new R package offers a systematic performance comparison with two other widely available R packages.

Evidence Used

Despite their widespread availability, comprehensive evaluations of these implementations under real--world data conditions remain limited.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

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.