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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. Presentation of algorithms is supplemented by case studies. The volume ends with a tutorial PCA deciphers genome.
Additional Information:
| Title | : Principal Manifolds for Data Visualization and Dimension Reduction |
| Author | : Alexander N. Gorban,Balázs Kégl,Donald C. Wunsch,Andrei Zinovyev |
| Item ID | : GgdoDUtUR8gC |
| Publisher | : Springer Science & Business Media |
| Type | : BOOK |
| Format File | : PDF & EPUB |
| Android/iOS | : Install “Google Play Books” |
| Page Count | : 340 |
| Category | : Technology & Engineering |
| Published Date | : 2007-09-11 |
| Language | : en |
| Isbn 10 | : 3540737502 |
| Isbn 13 | : 9783540737506 |
| Sale Info | : FOR_SALE |
| Price | : 207.99 EUR |
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