Randomized Nonnegative Matrix Factorization
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.
Cite
@article{arxiv.1711.02037,
title = {Randomized Nonnegative Matrix Factorization},
author = {N. Benjamin Erichson and Ariana Mendible and Sophie Wihlborn and J. Nathan Kutz},
journal= {arXiv preprint arXiv:1711.02037},
year = {2018}
}
Comments
This is an extended and revised version of the paper which appeared in JPRL