Principal manifolds for data visualization and dimension reduction

Principal manifolds for data visualization and dimension reduction / Alexander N. Gorban [and others], editors.

In 1901, Karl Pearson invented Principal Component Analysis (PCA). Since then, PCA serves as a prototype for many other tools of data analysis, visualization and dimension reduction: Independent Component Analysis (ICA), Multidimensional Scaling (MDS), Nonlinear PCA (NLPCA), Self Organizing Maps (SO...

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Bibliographic Details
Other Authors: Gorbanʹ, A. N. (Aleksandr Nikolaevich)
Format: eBook
Language:English
Published: Berlin : Springer, 2007.
Series:Lecture notes in computational science and engineering ; 58.
Subjects:
Principal components analysis.
Statistics > Graphic methods.
Dimension reduction (Statistics)
MATHEMATICS > Graphic Methods.
Mathematical Methods in Physics.
Numerical and Computational Methods in Engineering.
Estadística
Análisis en componentes principales
Principal components analysis
Statistics > Graphic methods
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