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.


References in zbMATH (referenced in 16 articles )

Showing results 1 to 16 of 16.
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  1. Behr, Merle; Munk, Axel: Statistical methods for minimax estimation in linear models with unknown design over finite alphabets (2022)
  2. Brusco, Michael J.; Steinley, Douglas: A variable neighborhood search heuristic for nonnegative matrix factorization with application to microarray data (2022)
  3. Martin Ondrus, Ivor Cribben: fabisearch: A Package for Change Point Detection in and Visualization of the Network Structure of Multivariate High-Dimensional Time Series in R (2022) arXiv
  4. Lim, David K.; Rashid, Naim U.; Ibrahim, Joseph G.: Model-based feature selection and clustering of RNA-seq data for unsupervised subtype discovery (2021)
  5. Gauchon, Romain; Loisel, Stéphane; Rullière, Jean-Louis: Health policyholder clustering using medical consumption. A useful tool for targeting prevention plans (2020)
  6. François Role, Stanislas Morbieu, Mohamed Nadif: CoClust: A Python Package for Co-Clustering (2019) not zbMATH
  7. Waegeman, Willem; Dembczyński, Krzysztof; Hüllermeier, Eyke: Multi-target prediction: a unifying view on problems and methods (2019)
  8. Barter, Rebecca L.; Yu, Bin: Superheat: an R package for creating beautiful and extendable heatmaps for visualizing complex data (2018)
  9. Krylov, V. V.: Odor space navigation using multisensory E-nose (2018)
  10. Stock, Michiel; Pahikkala, Tapio; Airola, Antti; De Baets, Bernard; Waegeman, Willem: A comparative study of pairwise learning methods based on kernel ridge regression (2018)
  11. Wang, Ketong; Porter, Michael D.: Optimal Bayesian clustering using non-negative matrix factorization (2018)
  12. Emily, Mathieu; Hitte, Christophe; Mom, Alain: SMILE: a novel dissimilarity-based procedure for detecting sparse-specific profiles in sparse contingency tables (2016)
  13. Ma, Junsheng; Stingo, Francesco C.; Hobbs, Brian P.: Bayesian predictive modeling for genomic based personalized treatment selection (2016)
  14. Michael Kane; John Emerson; Stephen Weston: Scalable Strategies for Computing with Massive Data (2013) not zbMATH
  15. Jiang, Xingpeng; Weitz, Joshua S.; Dushoff, Jonathan: A non-negative matrix factorization framework for identifying modular patterns in metagenomic profile data (2012)
  16. Janecek, Andreas; Schulze Grotthoff, Stefan; Gansterer, Wilfried N.: LibNMF -- a library for nonnegative matrix factorization (2011)