Items related to Geometric Structure of High-Dimensional Data and Dimensional...
Synopsis
"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.
"synopsis" may belong to another edition of this title.
- PublisherSpringer-Verlag New York Inc
- Publication date2012
- ISBN 10 364227496X
- ISBN 13 9783642274961
- BindingHardcover
- LanguageEnglish
- Number of pages356
Search results for Geometric Structure of High-Dimensional Data and Dimensional...
Seller Image
Geometric Structure of High-Dimensional Data and Dimensionality Reduction
Seller: killarneybooks, Inagh, CLARE, Ireland
Seller rating 5 out of 5 stars
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. -- The text delves into the complex world of high-dimensional data analysis and the mathematical techniques used for dimensionality reduction. It provides a comprehensive examination of the geometric and topological structures underlying high-dimensional datasets and offers advanced methodologies for reducing dimensionality while preserving essential data characteristics. Key topics and features of the book include: - Foundational Concepts (a thorough introduction to the principles of high-dimensional data, exploring the challenges and opportunities inherent in analyzing such data); - Geometric Structures (detailed analysis of the geometric properties of high-dimensional spaces, including manifold structures, metric spaces, and the role of curvature and topology in understanding data); - Dimensionality Reduction Techniques (an exploration of various techniques for reducing the dimensionality of data, such as Principal Component Analysis (PCA), Multidimensional Scaling (MDS), and t-Distributed Stochastic Neighbor Embedding (t-SNE)); -Mathematical Methods (in-depth coverage of the mathematical tools and frameworks used in dimensionality reduction, including linear algebra, differential geometry, and information theory); - Algorithmic Approaches (examination of algorithms designed to perform dimensionality reduction, focusing on their implementation, computational complexity, and practical applications); - Applications and Case Studies (real-world examples and case studies demonstrating the application of dimensionality reduction techniques in fields such as machine learning, image processing, bioinformatics, and data visualization); - Future Directions (insights into emerging trends and future research directions in the study of high-dimensional data and dimensionality reduction). This book is an essential resource for researchers, data scientists, and advanced students in fields such as machine learning, statistics, computer science, and applied mathematics. By combining theoretical foundations with practical applications, it provides readers with the tools and knowledge needed to tackle the complexities of high-dimensional data analysis and to develop effective dimensionality reduction strategies. Seller Inventory # 009829
Quantity: 1 available