Nonnegative Matrix Factorization (NMF) with Heteroscedastic Uncertainties and Missing data
Abstract
Dimensionality reduction and matrix factorization techniques are important and useful machine-learning techniques in many fields. Nonnegative matrix factorization (NMF) is particularly useful for spectral analysis and image processing in astronomy. I present the vectorized update rules and an independent proof of their convergence for NMF with heteroscedastic measurements and missing data. I release a Python implementation of the rules and use an optical spectroscopic dataset of extragalactic sources as an example for demonstration. A future paper will present results of applying the technique to image processing of planetary disks.
Cite
@article{arxiv.1612.06037,
title = {Nonnegative Matrix Factorization (NMF) with Heteroscedastic Uncertainties and Missing data},
author = {Guangtun Zhu},
journal= {arXiv preprint arXiv:1612.06037},
year = {2016}
}
Comments
Vectorized update rules for NMF with heteroscedastic measurements and the proof. The code NonnegMFPy is available at https://github.com/guangtunbenzhu/NonnegMFPy and can be installed through PyPI. Comments are most welcome