WICA: nonlinear weighted ICA
Machine Learning
2020-12-11 v2 Machine Learning
Abstract
Independent Component Analysis (ICA) aims to find a coordinate system in which the components of the data are independent. In this paper we construct a new nonlinear ICA model, called WICA, which obtains better and more stable results than other algorithms. A crucial tool is given by a new efficient method of verifying nonlinear dependence with the use of computation of correlation coefficients for normally weighted data. In addition, authors propose a new baseline nonlinear mixing to perform comparable experiments, and a~reliable measure which allows fair comparison of nonlinear models. Our code for WICA is available on Github https://github.com/gmum/wica.
Keywords
Cite
@article{arxiv.2001.04147,
title = {WICA: nonlinear weighted ICA},
author = {Andrzej Bedychaj and Przemysław Spurek and Aleksandra Nowak and Jacek Tabor},
journal= {arXiv preprint arXiv:2001.04147},
year = {2020}
}