This paper summarizes a new concept to determine principal curves for nonlinear principal component analysis (PCA). The concept is explained within the framework of the Hastie and Stuetzle algorithm and utilizes spline functions. The paper proposes a new algorithm and shows that it provides an efficient method to extract underlying information from measured data. The new method is geometrically simple and computationally expedient, as the number of unknown parameters increases linearly with the analyzed variable set. The utility of the algorithm is exemplified in two examples. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Antory, D., Kruger, U., & Littler, T. (2006). A new principal curve algorithm for nonlinear principal component analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4113 LNCS-I, pp. 1235–1246). Springer Verlag. https://doi.org/10.1007/11816157_155
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