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
Tabla de Contenidos:
  • Front Matter; Developments and Applications of Nonlinear Principal Component Analysis
  • a Review; Nonlinear Principal Component Analysis: Neural Network Models and Applications; Learning Nonlinear Principal Manifolds by Self-Organising Maps; Elastic Maps and Nets for Approximating Principal Manifolds and Their Application to Microarray Data Visualization; Topology-Preserving Mappings for Data Visualisation; The Iterative Extraction Approach to Clustering; Representing Complex Data Using Localized Principal Components with Application to Astronomical Data.

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