Nonlinearity Test for Principal Component Analysis

Attached code is developed to test whether the underlying structure within the data is nonlinear.

(( A Nonlinearity Test for Principal Component Analysis ))
** Attached MATLAB code is developed to test whether the underlying structure within the recorded data is linear or nonlinear.

Developers:
Dr. Ali Habibnia
London School of Economics
[e-mail]

Eghbal Rahimikia
Iran University of Science and Technology
[e-mail]

Hossein Mahdikhah
University of Tehran
[e-mail]

Files:

* Main function: pca_nl_test.m
* Core (related) function: pca_nl_core_test.m
* Demo: Demo.m
* Otimization functions: f_1.m and f_2.m
* README: readme (this file)

Reference papers:

* Kruger, U., Antory, D., Hahn, J., Irwin, G. and McCullough, G. (2005). Introduction of a nonlinearity measure for principal component models. Computers & Chemical Engineering, 29(11-12), pp.2355-2362.
* Kruger U., Zhang J., Xie L. (2008) Developments and Applications of Nonlinear Principal Component Analysis – a Review. In: Gorban A.N., Kégl B., Wunsch D.C., Zinovyev A.Y. (eds) Principal Manifolds for Data Visualization and Dimension Reduction. Lecture Notes in Computational Science and Enginee, vol 58. Springer, Berlin, Heidelberglications of nonlinear principal component analysis – a review

** Copyright 2018
** Cite: Habibnia, A., Rahimikia, E., & Mahdikhah, H. (2018). A Nonlinearity Test for Principal Component Analysis, MATLAB Central File Exchange. Retrieved Feb, 2018.
** Last Revision: Feb-2018 (Version 1.0)