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The Ethereal
NIMFA: A Python Library for Nonnegative Matrix Factorization
August 06, 2018 ยท Declared Dead ยท ๐ Journal of machine learning research
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Marinka Zitnik, Blaz Zupan
arXiv ID
1808.01743
Category
cs.LG: Machine Learning
Cross-listed
cs.AI,
q-bio.QM,
stat.ML
Citations
94
Venue
Journal of machine learning research
Last Checked
3 months ago
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.
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