Editors:
Part of the book series: Lecture Notes in Computational Science and Engineering (LNCSE, volume 58)
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Table of contents (14 papers)
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Front Matter
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Back Matter
About this book
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.
Editors and Affiliations
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University of Leicester, Leicester, UK
Alexander N. Gorban
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University of Paris-Sud - CNRS, Orsay, France
Balázs Kégl
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University of Missouri - Rolla, Rolla, USA
Donald C. Wunsch
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Institut Curie Service Bioinformatique, Paris, France
Andrei Y. Zinovyev
Bibliographic Information
Book Title: Principal Manifolds for Data Visualization and Dimension Reduction
Editors: Alexander N. Gorban, Balázs Kégl, Donald C. Wunsch, Andrei Y. Zinovyev
Series Title: Lecture Notes in Computational Science and Engineering
DOI: https://doi.org/10.1007/978-3-540-73750-6
Publisher: Springer Berlin, Heidelberg
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2008
Softcover ISBN: 978-3-540-73749-0Published: 01 October 2007
eBook ISBN: 978-3-540-73750-6Published: 11 September 2007
Series ISSN: 1439-7358
Series E-ISSN: 2197-7100
Edition Number: 1
Number of Pages: XXIV, 340
Number of Illustrations: 68 b/w illustrations, 14 illustrations in colour
Topics: Control and Systems Theory, Science, Humanities and Social Sciences, multidisciplinary, Computational Science and Engineering, Mathematical Methods in Physics, Computational Intelligence, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences