NIMFA: A Python Library for Nonnegative Matrix Factorization
Machine Learning
2018-08-07 v1 Artificial Intelligence
Quantitative Methods
Machine Learning
Abstract
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.
Cite
@article{arxiv.1808.01743,
title = {NIMFA: A Python Library for Nonnegative Matrix Factorization},
author = {Marinka Zitnik and Blaz Zupan},
journal= {arXiv preprint arXiv:1808.01743},
year = {2018}
}