# NIMFA: A Python Library for Nonnegative Matrix Factorization

arXiv: https://arxiv.org/abs/1808.01743 · HTML: https://mlsanity.com/paper/1808.01743/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2018.

## Built on by (most cited first, 10 of 10)

- [Graph-based Neural Acceleration for Nonnegative Matrix Factorization](https://mlsanity.com/paper/2202.00264/) (2022)
- [Quantifying the Conceptual Error in Dimensionality Reduction](https://mlsanity.com/paper/2106.06815/) (2021)
- [Efficient and Accurate Learning of Mixtures of Plackett-Luce Models](https://mlsanity.com/paper/2302.05343/) (2023)
- [Fast (1+varepsilon)-Approximation Algorithms for Binary Matrix Factorization](https://mlsanity.com/paper/2306.01869/) (2023)
- [No Pattern, No Recognition: a Survey about Reproducibility and Distortion Issues of Text Clustering and Topic Modeling](https://mlsanity.com/paper/2208.01712/) (2022)
- [Multi-Channel Automatic Music Transcription Using Tensor Algebra](https://mlsanity.com/paper/2107.11250/) (2021)
- [Compressed representation of brain genetic transcription](https://mlsanity.com/paper/2310.16113/) (2023)
- [Verifying Relational Explanations: A Probabilistic Approach](https://mlsanity.com/paper/2401.02703/) (2024)
- [Rank Suggestion in Non-negative Matrix Factorization: Residual Sensitivity to Initial Conditions (RSIC)](https://mlsanity.com/paper/2410.14838/) (2024)
- [Non-negative Matrix Factorisation with Topological Regularisation](https://mlsanity.com/paper/2606.17531/) (2026)

## Abstract (arXiv)

NIMFA is an open-source Python library that provides a unified interface to nonnegative matrix factorization algorithms. It includes implementations of state-of-the-art factorization methods, initialization approaches, and quality scoring. It supports both dense and sparse matrix representation. NIMFA's component-based implementation and hierarchical design should help the users to employ already implemented techniques or design and code new strategies for matrix factorization tasks.