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...

Descripción completa

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.
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 preserving mappings are described as well. Presentation of algorithms is supplemented by case studies, from engineering to astronomy, but mostly of biological data: analysis of microarray and metabolite data. The volume ends with a tutorial "PCA and K-means decipher genome". The book is meant to be useful for practitioners in applied data analysis in life sciences, engineering, physics and chemistry; it will also be valuable to PhD students and researchers in computer sciences, applied mathematics and statistics.
Descripción Física:XXIV, 340 p. 82 illus., 14 illus. in color. online resource.
ISBN:9783540737506
ISSN:2197-7100 ;

Ejemplares similares