Principal Manifolds for Data Visualization and Dimension Reduction - Lecture Notes in Computational Science and Engineering 58 (Paperback)A.N. Gorban (volume editor), Balazs Kegl (volume editor), D.C. Wunsch, II (volume editor), Andrey Zinovyev (volume editor)
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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.
Publisher: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Number of pages: 340
Weight: 557 g
Dimensions: 235 x 155 x 13 mm
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