Principal Manifolds for Data Visualization and Dimension Reduction

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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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor Corporativo: SpringerLink (Online service)
Otros Autores: Gorban, Alexander N. (Editor ), Kégl, Balázs (Editor ), Wunsch, Donald C. (Editor ), Zinovyev, Andrei (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2008.
Edición:1st ed. 2008.
Colección:Lecture Notes in Computational Science and Engineering, 58
Temas:
Control engineering.
Social sciences.
Humanities.
Mathematics-Data processing.
Mathematical physics.
Computational intelligence.
Statistics .
Control and Systems Theory.
Humanities and Social Sciences.
Computational Science and Engineering.
Mathematical Methods in Physics.
Computational Intelligence.
Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
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