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arXiv · 1711.02037

Randomized Nonnegative Matrix Factorization

N. Benjamin Erichson·Ariana Mendible·Sophie Wihlborn·J. Nathan Kutz
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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 applications while attaining a near-optimal factorization. The proposed algorithm is evaluated using synthetic and real world data and shows substantial speedups compared to deterministic HALS.

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N. Benjamin Erichson, Ariana Mendible, Sophie Wihlborn, J. Nathan Kutz. 2018-04-30. Randomized Nonnegative Matrix Factorization. https://doi.org/10.1016/j.patrec.2018.01.007

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