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"Geometric Structure of High-Dimensional Data and Dimensionality Reduction" adopts data geometry as a framework to address various methods of dimensionality reduction. In addition to the introduction to well-known linear methods, the book moreover stresses the recently developed nonlinear methods and introduces the applications of dimensionality reduction in many areas, such as face recognition, image segmentation, data classification, data visualization, and hyperspectral imagery data analysis. Numerous tables and graphs are included to illustrate the ideas, effects, and shortcomings of the methods. MATLAB code of all dimensionality reduction algorithms is provided to aid the readers with the implementations on computers.
The book will be useful for mathematicians, statisticians, computer scientists, and data analysts. It is also a valuable handbook for other practitioners who have a basic background in mathematics, statistics and/or computer algorithms, like internet search engine designers, physicists, geologists, electronic engineers, and economists.
Jianzhong Wang is a Professor of Mathematics at Sam Houston State University, U.S.A.
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- PublisherSpringer
- Publication date2012
- ISBN 10 364227496X
- ISBN 13 9783642274961
- BindingHardcover
- Number of pages365
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Geometric Structure of High-Dimensional Data and Dimensionality Reduction
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Book Description Hardcover. Condition: Very Good. Hardcover, xx + 356 pages, NOT ex-library. Front pastedown with a tiny split in gutter (lower end). Book shows little wear, clean and bright throughout with unmarked text, free of inscriptions and stamps, firmly bound. Boards with some short creases to edges. Issued without a dust jacket. Seller Inventory # 007584
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