论文标题
WICA：非线性加权ICA
WICA: nonlinear weighted ICA
论文作者
论文摘要
独立的组件分析（ICA）旨在找到一个坐标系，其中数据的组件是独立的。在本文中，我们构建了一种称为WICA的新的非线性ICA模型，该模型比其他算法更好，更稳定。一种至关重要的工具是通过使用正常加权数据的相关系数的计算来验证非线性依赖性的新有效方法。此外，作者提出了一种新的基线非线性混合以执行可比的实验，并提出了可靠的措施，可以公平地比较非线性模型。我们的WICA代码可在github https://github.com/gmum/wica上找到。
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.
