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

This research presents a new R package for Non-negative Matrix Factorization (NMF) and compares it with two other widely used NMF packages in R. The comparison is based on real-world data instead of simulated datasets, focusing on aspects like computational efficiency, convergence behavior, reconstruction accuracy, memory utilization, and the stability of the resulting matrix factorization.

What the Document Says

The paper discusses Non-negative Matrix Factorization (NMF), a technique used for dimensionality reduction and extraction of latent structures from non-negative data.
It mentions that NMF has applications in various fields such as bioinformatics, text mining, image analysis, and recommender systems.
The authors note that there are numerous R packages implementing different optimization strategies and computational frameworks for NMF.
Despite the availability of these packages, comprehensive evaluations under real-world data conditions are limited, making it challenging for researchers to choose an appropriate package for practical applications.
The study introduces a new R package for NMF and conducts a systematic performance comparison with two widely available R packages for NMF analysis.
The evaluation is based on real-world data instead of simulated datasets to better reflect the complexity, heterogeneity, and noise characteristics encountered in practical analytical settings.
The focus areas for comparison are 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
latent structures
bioinformatics
text mining
image analysis
recommender systems

Claims Made by the Document

There are numerous R packages for NMF with different optimization strategies and computational frameworks.
Comprehensive evaluations of these implementations under real-world data conditions are limited.
The new R package introduced in the study offers a systematic performance comparison with two other widely used R packages for NMF analysis.
Real-world data is used instead of simulated datasets for the evaluation.

Evidence Used

Discussion on the popularity and applications of NMF
Mention of numerous R packages for NMF
Statement about limited comprehensive evaluations
Description of the new R package and comparison with others
Explanation of using real-world data instead of simulated datasets

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.