Algorithms for Non-Negative Matrix Factorization on Noisy Data With Negative Values

arXiv:2311.04855v2 Announce Type: replace-cross
Abstract: Non-negative matrix factorization (NMF) is a dimensionality reduction technique that has shown promise for analyzing noisy data, especially astronomical data. For these datasets, the observed data may contain negative values due to noise even when the true underlying physical signal is strictly positive. Prior NMF work has not treated negative data in a statistically consistent manner, which becomes problematic for low signal-to-noise data with many negative values. In this paper we present two algorithms, Shift-NMF …

abstract algorithms arxiv astro-ph.im cs.lg data datasets dimensionality eess.sp factorization matrix negative noise positive prior signal stat.me true type values