[PDF.83wa] Nonlinear Dimensionality Reduction (Information Science and Statistics)

Nonlinear Dimensionality Reduction (Information Science and Statistics)

[PDF.df85] Nonlinear Dimensionality Reduction (Information Science and Statistics)

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 | #1651315 in Books |  2007-12-05 |  2007-12-05 | Original language:English | PDF # 1 |  9.25 x.74 x6.10l,1.84 | File type: PDF | 309 pages

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||13 of 13 people found the following review helpful.| A detailed work for Manifold Learning|By Customer|It covers the major Manifold and in general Nonlinear dimensionality reduction, such as ISOMAP, Locally Linear Embedding, Laplacian Eigenmaps, Maximum Variance Unfolding, Multidimensional scaling. I think the presentation of the methods is good and very well illustrated. The authors have done pretty good job in terms of figur| ||From the reviews: |"This beautifully produced book covers various innovative topics in nonlinear dimensionality reduction, such as Isomap, locally linear embedding, and Laplacian eigenmaps, etc. Those topics are usually not covered by existing texts on multiv

This book describes established and advanced methods for reducing the dimensionality of numerical databases. Each description starts from intuitive ideas, develops the necessary mathematical details, and ends by outlining the algorithmic implementation. The text provides a lucid summary of facts and concepts relating to well-known methods as well as recent developments in nonlinear dimensionality reduction. Methods are all described from a unifying point of view, whic...

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