Principal manifolds for data visualization and dimension reduction

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 m...

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Bibliographic Details
Main Authors: Gorban, Alexander N., Kégl, Balázs, Wunsch, Donald C., Zinovyev, Andrei
Format: eBook Book
Language:English
Published: Berlin, Heidelberg Springer 2008
Springer Berlin / Heidelberg
Springer Berlin Heidelberg
Edition:1
Series:Lecture Notes in Computational Science and Enginee
Subjects:
Computational Intelligence
Computational Science and Engineering
Control and Systems Theory
Data analysis
Engineering
Mathematical Methods in Physics
Mathematics and Statistics
Methodology
Principal components analysis
Science (General)
Science, Humanities and Social Sciences, multidisciplinary
Statistics
Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
ISBN:3540737499, 9783540737490
ISSN:1439-7358
Online Access:Get full text
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Summary: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. Presentation of algorithms is supplemented by case studies. The volume ends with a tutorial PCA deciphers genome.
Bibliography:Includes bibliographical references and index
Other editors: Balázs Kégl, Donald C. Wunsch, Andrei Zinovyev
SourceType-Books-1
ObjectType-Book-1
content type line 7
ISBN:3540737499
9783540737490
ISSN:1439-7358
DOI:10.1007/978-3-540-73750-6