# 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} }

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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