Principal Manifolds for Data Visualization and Dimension Reduction

Name: Principal Manifolds for Data Visualization and Dimension Reduction
SKU: 3853871
Price: 585.99 AUD
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Paperback / softback

Series:

Lecture Notes in Computational Science and Engineering

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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-means 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.

Release date Australia

October 1st, 2007

Audience

Postgraduate, Research & Scholarly

Contributors

Edited by Alexander N Gorban
Edited by Andrei Zinovyev
Edited by Balazs Kegl
Edited by Donald C. Wunsch

Illustrations

14 Illustrations, color; 68 Illustrations, black and white; XXIV, 340 p. 82 illus., 14 illus. in color.

Pages

340

Series

Lecture Notes in Computational Science and Engineering

Dimensions

155x235x13

ISBN-13

9783540737490

Product ID

3853871

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Principal Manifolds for Data Visualization and Dimension Reduction

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