Gunsilius, Florian; Schennach, Susanne M. - 2017 - This version: March 27, 2017
The aim of this paper is to introduce a practical nonlinear generalization of PCA that captures nonlinear forms of dependence and delivers truly independent factors. The output of the method is a low-dimensional curvilinear coordinate system that tracks the important features of the data. The...