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
Otros Autores: Gorbanʹ, A. N. (Aleksandr Nikolaevich)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berlin : Springer, 2007.
Colección:Lecture notes in computational science and engineering ; 58.
Temas:
Principal components analysis.
Statistics > Graphic methods.
Dimension reduction (Statistics)
Analyse en composantes principales.
Statistique > Méthodes graphiques.
Réduction de dimension (Statistique)
MATHEMATICS > Graphic Methods.
Mathematical Methods in Physics.
Numerical and Computational Methods in Engineering.
Estadística
Análisis en componentes principales
Principal components analysis
Statistics > Graphic methods
Acceso en línea:Texto completo
Descripción
Sumario: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 (SOM), etc. The book starts with the quote of the classical Pearson definition of PCA and includes reviews of various methods: NLPCA, ICA, MDS, embedding and clustering algorithms, principal manifolds and SOM. New approaches to NLPCA, principal manifolds, branching principal components and topology pre.
Descripción Física:1 online resource (xxiii, 334 pages) : illustrations (some color)
Bibliografía:Includes bibliographical references and index.
ISBN:9783540737506
3540737502
9783540737490
3540737499

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