Randomized Nonnegative Matrix Factorization
N. Benjamin Erichson, Ariana Mendible, Sophie Wihlborn, J. Nathan Kutz
Code Available — Be the first to reproduce this paper.
ReproduceCode
- github.com/Benli11/ristrettoOfficialIn papernone★ 118
- github.com/erichson/ristrettoOfficialIn papernone★ 0
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.