Nonlinear Principal Component Analysis: Neural Network Models and Applications. Scholz, M., Fraunholz, M., & Selbig, J. In Gorban, A. N., Kégl, B., Wunsch, D. C., & Zinovyev, A. Y., editors, volume 58, pages 44–67. Springer Berlin Heidelberg. ![Nonlinear Principal Component Analysis: Neural Network Models and Applications [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Nonlinear principal component analysis (NLPCA) as a nonlinear generalisation of standard principal component analysis (PCA) means to generalise the principal components from straight lines to curves. This chapter aims to provide an extensive description of the autoassociative neural network approach for NLPCA. Several network architectures will be discussed including the hierarchical, the circular, and the inverse model with special emphasis to missing data. Results are shown from applications in the field of molecular biology. This includes metabolite data analysis of a cold stress experiment in the model plant Arabidopsis thaliana and gene expression analysis of the reproductive cycle of the malaria parasite Plasmodium falciparum within infected red blood cells.
@incollection{scholzNonlinearPrincipalComponent2008,
title = {Nonlinear Principal Component Analysis: Neural Network Models and Applications},
author = {Scholz, Matthias and Fraunholz, Martin and Selbig, Joachim},
editor = {Gorban, Alexander N. and Kégl, Balázs and Wunsch, Donald C. and Zinovyev, Andrei Y.},
date = {2008},
volume = {58},
pages = {44--67},
publisher = {{Springer Berlin Heidelberg}},
location = {{Berlin, Heidelberg}},
doi = {10.1007/978-3-540-73750-6\\_2},
url = {https://doi.org/10.1007/978%2d3%2d540%2d73750%2d6%5f2},
abstract = {Nonlinear principal component analysis (NLPCA) as a nonlinear generalisation of standard principal component analysis (PCA) means to generalise the principal components from straight lines to curves. This chapter aims to provide an extensive description of the autoassociative neural network approach for NLPCA. Several network architectures will be discussed including the hierarchical, the circular, and the inverse model with special emphasis to missing data. Results are shown from applications in the field of molecular biology. This includes metabolite data analysis of a cold stress experiment in the model plant Arabidopsis thaliana and gene expression analysis of the reproductive cycle of the malaria parasite Plasmodium falciparum within infected red blood cells.},
isbn = {978-3-540-73749-0},
keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-13491337,artificial-neural-networks,mathematics,non-linearity,nonlinear-correlation,pca}
}
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