Geometric Structure of High-Dimensional Data and Dimensionality Reduction - Couverture rigide

Wang, Jianzhong

Couverture rigide

ISBN 10 : 364227496X ISBN 13 : 9783642274961

Editeur : Springer-Verlag Berlin and Heidelberg GmbH & Co. K, 2012

Synopsis

Geometric Structure of High-Dimensional Data and Dimensionality Reduction Adopting data geometry as a framework to address dimensionality reduction, this volume introduces well known linear methods, stressing recently developed ones, and covers various dimensionality reduction applications including hyperspectral imagery. Full description

A propos du livre

Présentation de l'éditeur :

"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.

À propos de cette édition

• Éditeur: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
• Date d'édition: 2012
• ISBN 10: 364227496X
• ISBN 13: 9783642274961
• Reliure: Relié
• Nombre de pages: 356