Faster-than-fast NMF using random projections and Nesterov iterations

Abstract

Random projections have been recently implemented in Nonnegative Matrix Factorization (NMF) to speed-up the NMF computations, with a negligible loss of performance. In this paper, we investigate the effects of such projections when the NMF technique uses the fast Nesterov gradient descent (NeNMF). We experimentally show the randomized subspace iteration to significantly speed-up NeNMF.

arXiv commentin Proceedings of iTWIST'18, Paper-ID: 28, Marseille, France, November, 21-23, 2018

References in corpus (2)

• Randomized Nonnegative Matrix Factorization
• Compressed Nonnegative Matrix Factorization is Fast and Accurate

Cited by in corpus (1)

• Gaussian Compression Stream: Principle and Preliminary Results