NIMFA: A Python Library for Nonnegative Matrix Factorization
This is an incremental tool for researchers and practitioners in machine learning and data analysis, facilitating easier implementation and experimentation with matrix factorization techniques.
The authors tackled the lack of a unified Python library for nonnegative matrix factorization by developing NIMFA, which provides a comprehensive interface with state-of-the-art algorithms, initialization methods, and support for dense and sparse matrices.
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.