Autor:	Zitnik, Marinka, Zupan, Blaz
Rok vydání:	2018
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Quantitative Biology - Quantitative Methods Statistics - Machine Learning
Zdroj:	Journal of Machine Learning Research 13 (2012) 849-853
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1808.01743 Zobrazit plný text záznamu View this record from Arxiv