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arXiv:1808.01743cs.LG

NIMFA: A Python Library for Nonnegative Matrix Factorization

Marinka Zitnik, Blaz Zupan

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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.

arXiv comments
Journal reference
Journal of Machine Learning Research 13 (2012) 849-853
DOI
Subjects
cs.LGMachine Learning cs.AIArtificial Intelligence q-bio.QMQuantitative Methods stat.MLMachine Learning (Statistics)

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