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Principal Manifolds for Data Visualization and Dimension Reduction

Principal Manifolds for Data Visualization and Dimension Reduction

by Alexander N. GorbanBalazs Kegl Donald C. Wunsch and others
Paperback
Publication Date: 01/10/2007

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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.
ISBN:
9783540737490
9783540737490
Category:
Automatic control engineering
Format:
Paperback
Publication Date:
01-10-2007
Language:
English
Publisher:
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Country of origin:
Germany
Pages:
340
Dimensions (mm):
235x155x13mm
Weight:
0.56kg

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