1 Developments and Applications of Nonlinear Principal Component Analysis – a Review

@inproceedings{Kruger20071DA,
title={1 Developments and Applications of Nonlinear Principal Component Analysis – a Review},
author={Uwe Kruger and Junping Zhang and Lei Xie},
year={2007}
}

Although linear principal component analysis (PCA) originates from the work of Sylvester [67] and Pearson [51], the development of nonlinear counterparts has only received attention from the 1980s. Work on nonlinear PCA, or NLPCA, can be divided into the utilization of autoassociative neural networks, principal curves and manifolds, kernel approaches or the combination of these approaches. This article reviews existing algorithmic work, shows how a given data set can be examined to determine… CONTINUE READING