Principal Manifolds for Data Visualization and Dimension Reduction

Released on by Alexander N. Gorban
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  • Principal Manifolds for Data Visualization and Dimension Reduction Book Detail

  • Author : Alexander N. Gorban
  • Release Date : 2007-09-11
  • Publisher : Springer Science & Business Media
  • Genre : Technology & Engineering
  • Pages : 361
  • ISBN 13 : 3540737502
  • File Size : 50,50 MB
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Principal Manifolds for Data Visualization and Dimension Reduction by Alexander N. Gorban PDF Summary

Book 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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