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Principal Manifolds for Data Visualization and Dimension Reduction

indgår i Lecture Notes in Computational Science and Engineering serien

Principal Manifolds for Data Visualization and Dimension Reduction
Bag om Principal Manifolds for Data Visualization and Dimension Reduction

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.

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  • Sprog:
  • Engelsk
  • ISBN:
  • 9783540737490
  • Indbinding:
  • Paperback
  • Sideantal:
  • 340
  • Udgivet:
  • 1. oktober 2007
  • Udgave:
  • 2008
  • Størrelse:
  • 235x155x13 mm.
  • Vægt:
  • 557 g.
  • 8-11 hverdage.
  • 13. december 2024
Forlænget returret til d. 31. januar 2025

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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.

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