Track papers across labs, topics, and trends.
Search papers, labs, and topics across Lattice.
This study evaluates the performance of a newly introduced R package for non-negative matrix factorization (NMF) against two existing packages, focusing on real-world data to provide a more accurate assessment of their capabilities. The evaluation criteria include computational efficiency, convergence behavior, reconstruction accuracy, memory utilization, and stability of the matrix factorization results. Key findings reveal significant differences in performance metrics, offering researchers valuable insights for selecting the most suitable NMF implementation for their specific applications.
Real-world data reveals stark performance differences among popular NMF R packages, guiding researchers to make informed choices in their analyses.
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.