NIMFA: A Python Library for Nonnegative Matrix Factorization
Not on the map. Why?
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.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Aug 2018.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 2; 2022: 2; 2023: 3; 2024: 2; 2025: 0; 2026: 1.
10 papers.
- 2022Graph-based Neural Acceleration for Nonnegative Matrix Factorizationcited by 9
- 2021Quantifying the Conceptual Error in Dimensionality Reductioncited by 6
- 2023Efficient and Accurate Learning of Mixtures of Plackett-Luce Modelscited by 3
- 2023Fast (1+varepsilon)-Approximation Algorithms for Binary Matrix Factorizationcited by 3
- 2022No Pattern, No Recognition: a Survey about Reproducibility and Distortion Issues of Text Clustering and Topic Modelingcited by 2
- 2021Multi-Channel Automatic Music Transcription Using Tensor Algebracited by 0
- 2023Compressed representation of brain genetic transcriptioncited by 0
- 2024Verifying Relational Explanations: A Probabilistic Approachcited by 0
- 2024Rank Suggestion in Non-negative Matrix Factorization: Residual Sensitivity to Initial Conditions (RSIC)cited by 0
- 2026Non-negative Matrix Factorisation with Topological Regularisationcited by 0
- 2026Non-negative Matrix Factorisation with Topological Regularisation16 Jun 2026
- 2024Rank Suggestion in Non-negative Matrix Factorization: Residual Sensitivity to Initial Conditions (RSIC)18 Oct 2024
- 2024Verifying Relational Explanations: A Probabilistic Approach5 Jan 2024
- 2023Compressed representation of brain genetic transcription24 Oct 2023
- 2023Fast (1+varepsilon)-Approximation Algorithms for Binary Matrix Factorization2 Jun 2023
- 2023Efficient and Accurate Learning of Mixtures of Plackett-Luce Models10 Feb 2023
- 2022No Pattern, No Recognition: a Survey about Reproducibility and Distortion Issues of Text Clustering and Topic Modeling2 Aug 2022
- 2022Graph-based Neural Acceleration for Nonnegative Matrix Factorization1 Feb 2022
- 2021Multi-Channel Automatic Music Transcription Using Tensor Algebra23 Jul 2021
- 2021Quantifying the Conceptual Error in Dimensionality Reduction12 Jun 2021