GERMAN

Principal Manifolds for Data Visualization and Dimension Reduction (Lecture Notes in Computational Science and Engineering)

By Alexander N. Gorban , Balázs Kégl , Donald C. Wunsch , Andrei Zinovyev

Book information

Publisher
Springer
Year
2007
ISBN
3540737499, 9783540737490, 1082582646
Open Library ID
OL16155443M
Language
german
Format
DJVU
Filesize
4 MB (4175462 bytes)
Series
Lecture Notes in Computational Science and Engineering
Edition
1
Pages
367\367
Time added
2010-02-18 13:16:04

Description

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-meansВ 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.

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