Nonlinear Dimensionality Reduction (Hardcover, 2007 ed.)

Methods of dimensionality reduction provide a way to understand and visualize the structure of complex data sets. Traditional methods like principal component analysis and classical metric multidimensional scaling suffer from being based on linear models. Until recently, very few methods were able to reduce the data dimensionality in a nonlinear way. However, since the late nineties, many new methods have been developed and nonlinear dimensionality reduction, also called manifold learning, has become a hot topic. New advances that account for this rapid growth are, e.g. the use of graphs to represent the manifold topology, and the use of new metrics like the geodesic distance. In addition, new optimization schemes, based on kernel techniques and spectral decomposition, have lead to spectral embedding, which encompasses many of the

General

Imprint:	Springer-Verlag New York
Country of origin:	United States
Series:	Information Science and Statistics
Release date:	December 2007
First published:	November 2007
Authors:	John A. Lee • Michel Verleysen
Dimensions:	240 x 160 x 24mm (L x W x T)
Format:	Hardcover
Pages:	308
Edition:	2007 ed.
ISBN-13:	978-0-387-39350-6
Categories:	Books > Science & Mathematics > Mathematics > Mathematical foundations > Mathematical logic Promotions
LSN:	0-387-39350-1
Barcode:	9780387393506

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