Principal Manifolds for Data Visualization and Dimension Reduction
| Author | : Alexander N. Gorban |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 361 |
| Release | : 2007-10 |
| ISBN-10 | : 9783540737490 |
| ISBN-13 | : 3540737499 |
| Rating | : 4/5 (90 Downloads) |
Book Synopsis Principal Manifolds for Data Visualization and Dimension Reduction by : Alexander N. Gorban
Download or read book Principal Manifolds for Data Visualization and Dimension Reduction written by Alexander N. Gorban and published by Springer Science & Business Media. This book was released on 2007-10 with total page 361 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
