WICA: nonlinear weighted ICA
This work addresses the challenge of nonlinear dependence in data analysis for researchers in machine learning and signal processing, representing an incremental improvement with novel verification and comparison methods.
The authors tackled the problem of nonlinear Independent Component Analysis (ICA) by introducing WICA, a new nonlinear ICA model that achieves better and more stable results than other algorithms, as demonstrated through comparable experiments with a new baseline mixing and reliable comparison measure.
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.