NMF

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

Anything in here will be replaced on browsers that support the canvas element

References in zbMATH (referenced in 8 articles )

Showing results 1 to 8 of 8.
Sorted by year (citations)

  1. François Role, Stanislas Morbieu, Mohamed Nadif: CoClust: A Python Package for Co-Clustering (2019) not zbMATH
  2. Krylov, V. V.: Odor space navigation using multisensory E-nose (2018)
  3. Wang, Ketong; Porter, Michael D.: Optimal Bayesian clustering using non-negative matrix factorization (2018)
  4. Emily, Mathieu; Hitte, Christophe; Mom, Alain: SMILE: a novel dissimilarity-based procedure for detecting sparse-specific profiles in sparse contingency tables (2016)
  5. Ma, Junsheng; Stingo, Francesco C.; Hobbs, Brian P.: Bayesian predictive modeling for genomic based personalized treatment selection (2016)
  6. Michael Kane; John Emerson; Stephen Weston: Scalable Strategies for Computing with Massive Data (2013) not zbMATH
  7. Jiang, Xingpeng; Weitz, Joshua S.; Dushoff, Jonathan: A non-negative matrix factorization framework for identifying modular patterns in metagenomic profile data (2012)
  8. Janecek, Andreas; Schulze Grotthoff, Stefan; Gansterer, Wilfried N.: LibNMF -- a library for nonnegative matrix factorization (2011)