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WICA: nonlinear weighted ICA

2020-01-13 · Andrzej Bedychaj, Przemysław Spurek, Aleksandra Nowak, Jacek Tabor

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.

📄 PDF Abstract BibTeX arXiv:2001.04147

Code (1)

gmum/wica 공식 구현 pytorch

Methods 이 논문이 사용한 방법론

ICA _Independent component analysis (ICA) is a statistical and computational technique for revealing hidden factors that underlie sets of random variables, measurements, or…

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