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Erschienen in:
2008 | OriginalPaper | Buchkapitel
Nonlinear Principal Component Analysis: Neural Network Models and Applications
verfasst von : Matthias Scholz, Martin Fraunholz, Joachim Selbig
Erschienen in: Principal Manifolds for Data Visualization and Dimension Reduction
Verlag: Springer Berlin Heidelberg
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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.