R package NMF: Algorithms and framework for Nonnegative Matrix Factorization (NMF). This package provides a framework to perform Non-negative Matrix Factorization (NMF). It implements a set of already published algorithms and seeding methods, and provides a framework to test, develop and plug new/custom algorithms. Most of the built-in algorithms have been optimized in C++, and the main interface function provides an easy way of performing parallel computations on multicore machines.
Keywords for this software
References in zbMATH (referenced in 8 articles )
Showing results 1 to 8 of 8.
- François Role, Stanislas Morbieu, Mohamed Nadif: CoClust: A Python Package for Co-Clustering (2019) not zbMATH
- Krylov, V. V.: Odor space navigation using multisensory E-nose (2018)
- Wang, Ketong; Porter, Michael D.: Optimal Bayesian clustering using non-negative matrix factorization (2018)
- Emily, Mathieu; Hitte, Christophe; Mom, Alain: SMILE: a novel dissimilarity-based procedure for detecting sparse-specific profiles in sparse contingency tables (2016)
- Ma, Junsheng; Stingo, Francesco C.; Hobbs, Brian P.: Bayesian predictive modeling for genomic based personalized treatment selection (2016)
- Michael Kane; John Emerson; Stephen Weston: Scalable Strategies for Computing with Massive Data (2013) not zbMATH
- Jiang, Xingpeng; Weitz, Joshua S.; Dushoff, Jonathan: A non-negative matrix factorization framework for identifying modular patterns in metagenomic profile data (2012)
- Janecek, Andreas; Schulze Grotthoff, Stefan; Gansterer, Wilfried N.: LibNMF -- a library for nonnegative matrix factorization (2011)