Geometric Structure Of High Dimensional Data And Dimensionality Reduction
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Geometric Structure of High-Dimensional Data and Dimensionality Reduction
Author: Jianzhong Wang
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-04-28
"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.
Intelligent Information Processing XII
The two-volume set IFIP AICT 703 and 704 constitutes the refereed conference proceedings of the 13th IFIP TC 12 International Conference on Intelligent Information Processing XII, IIP 2024, held in Shenzhen, China, during May 3–6, 2024. The 49 full papers and 5 short papers presented in these proceedings were carefully reviewed and selected from 58 submissions. The papers are organized in the following topical sections: Volume I: Machine Learning; Natural Language Processing; Neural and Evolutionary Computing; Recommendation and Social Computing; Business Intelligence and Risk Control; and Pattern Recognition. Volume II: Image Understanding.