Non-linear ICA based on Cramer-Wold metric
Machine Learning
2020-11-24 v1 Machine Learning
Abstract
Non-linear source separation is a challenging open problem with many applications. We extend a recently proposed Adversarial Non-linear ICA (ANICA) model, and introduce Cramer-Wold ICA (CW-ICA). In contrast to ANICA we use a simple, closed--form optimization target instead of a discriminator--based independence measure. Our results show that CW-ICA achieves comparable results to ANICA, while foregoing the need for adversarial training.
Cite
@article{arxiv.1903.00201,
title = {Non-linear ICA based on Cramer-Wold metric},
author = {Przemysław Spurek and Aleksandra Nowak and Jacek Tabor and Łukasz Maziarka and Stanisław Jastrzębski},
journal= {arXiv preprint arXiv:1903.00201},
year = {2020}
}