Principal Manifolds for Data Visualization and Dimension Reduction Front Cover

Principal Manifolds for Data Visualization and Dimension Reduction

  • Length: 364 pages
  • Edition: 1
  • Publisher:
  • Publication Date: 2007-10-24
  • ISBN-10: 3540737499
  • ISBN-13: 9783540737490
  • Sales Rank: #4257153 (See Top 100 Books)
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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