Randomized nonnegative matrix factorization

@article{Erichson2018RandomizedNM,
  title={Randomized nonnegative matrix factorization},
  author={N. Erichson and Ariana Mendible and Sophie Wihlborn and J. N. Kutz},
  journal={ArXiv},
  year={2018},
  volume={abs/1711.02037}
}
  • N. Erichson, Ariana Mendible, +1 author J. N. Kutz
  • Published 2018
  • Computer Science, Mathematics
  • ArXiv
  • Abstract Nonnegative matrix factorization (NMF) is a powerful tool for data mining. However, the emergence of ‘big data’ has severely challenged our ability to compute this fundamental decomposition using deterministic algorithms. This paper presents a randomized hierarchical alternating least squares (HALS) algorithm to compute the NMF. By deriving a smaller matrix from the nonnegative input data, a more efficient nonnegative decomposition can be computed. Our algorithm scales to big data… CONTINUE READING
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