Principal Manifolds for Data Visualization and Dimension Reduction

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Synopsis

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.

Book details

Edition:
2008
Series:
Lecture Notes in Computational Science and Engineering (Book 58)
Author:
Alexander N. Gorban, Balázs Kégl, Donald C. Wunsch, Andrei Zinovyev
ISBN:
9783540737506
Related ISBNs:
9783540737490
Publisher:
Springer Berlin Heidelberg
Pages:
N/A
Reading age:
Not specified
Includes images:
No
Date of addition:
2022-08-28
Usage restrictions:
Copyright
Copyright date:
2008
Copyright by:
N/A 
Adult content:
No
Language:
English
Categories:
Computers and Internet, Earth Sciences, Mathematics and Statistics, Nonfiction, Science, Technology