Accelerated parallel and distributed algorithm using limited internal memory for nonnegative matrix factorization

@article{Nguyen2017AcceleratedPA,
  title={Accelerated parallel and distributed algorithm using limited internal memory for nonnegative matrix factorization},
  author={Duy Khuong Nguyen and Tu Bao Ho},
  journal={Journal of Global Optimization},
  year={2017},
  volume={68},
  pages={307-328}
}
  • D. Nguyen, T. Ho
  • Published 29 June 2015
  • Computer Science
  • Journal of Global Optimization
Nonnegative matrix factorization (NMF) is a powerful technique for dimension reduction, extracting latent factors and learning part-based representation. For large datasets, NMF performance depends on some major issues such as fast algorithms, fully parallel distributed feasibility and limited internal memory. This research designs a fast fully parallel and distributed algorithm using limited internal memory to reach high NMF performance for large datasets. Specially, we propose a flexible… 
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