Statistics, Numerical Analysis & Solutions
Principal Manifolds for Data Visualization and Dimension Reduction
Gorban, Alexander N., K. Gl, Bal Zs, Wunsch, Donald C., II
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Overview
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.
Book Details
Published
June 11, 2026
Publisher
Springer-Verlag New York, LLC
Pages
364
Format
Paperback
ISBN
9783540737490