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Principal Manifolds for Data Visualization and Dimension Reduction

E-bookPDFE-book
Ranking86755inTechnik
CHF271.50

Description

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.
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Details

Additional ISBN/GTIN9783540737506
Product TypeE-book
BindingE-book
FormatPDF
Format notewatermark
Publishing date11/09/2007
Edition2008
SeriesLecture Notes in Computational Science and Engineering
Series no.58
Pages340 pages
LanguageEnglish
IllustrationsXXIV, 340 p. 82 illus., 14 illus. in color.
Article no.1066441
CatalogsVC
Data source no.28925
Product groupTechnik
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