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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.
Pattern perception. --- Dimension reduction (Statistics) --- Computational complexity. --- Visualization. --- Dimensionality reduction (Statistics) --- Reduction, Dimension (Statistics) --- Reduction, Dimensionality (Statistics) --- Computer science. --- Data structures (Computer science). --- Mathematical statistics. --- Data mining. --- Applied mathematics. --- Engineering mathematics. --- Computer Science. --- Data Mining and Knowledge Discovery. --- Probability and Statistics in Computer Science. --- Applications of Mathematics. --- Data Structures, Cryptology and Information Theory. --- Complexity, Computational --- Electronic data processing --- Machine theory --- Design perception --- Pattern recognition --- Form perception --- Perception --- Figure-ground perception --- Visualisation --- Imagery (Psychology) --- Imagination --- Visual perception --- Statistics --- Mathematics. --- Data structures (Computer scienc. --- Data Structures and Information Theory. --- Math --- Science --- Informatics --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Information structures (Computer science) --- Structures, Data (Computer science) --- Structures, Information (Computer science) --- File organization (Computer science) --- Abstract data types (Computer science) --- Engineering --- Engineering analysis --- Mathematical analysis --- Mathematics --- Statistical inference --- Statistics, Mathematical --- Probabilities --- Sampling (Statistics) --- Statistical methods
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Operational research. Game theory --- Mathematics --- Computer science --- Information systems --- toegepaste wiskunde --- cryptologie --- stochastische analyse --- database management --- informatietechnologie --- programmatielogica
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This collection of 301 peer-reviewed papers reflects a meeting of academic research and industry applications, the sharing of R&D experience and the discussion of innovative achievements in the field of materials and manufacturing. It will not only furnish readers with a broad overview of the latest advances, but also provide a valuable summary and reference work for researchers in this field. Review from Book News Inc.: The 301 articles in this two-volume set are drawn from the International Conference on Materials and Manufacturing (ICMM), held in Jinzhou, China, in September 2011, and are m
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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.
Operational research. Game theory --- Mathematics --- Computer science --- Information systems --- toegepaste wiskunde --- cryptologie --- stochastische analyse --- database management --- informatietechnologie --- programmatielogica
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An in-depth look at real analysis and its applications, including an introduction to waveletanalysis, a popular topic in ""applied real analysis"". This text makes a very natural connection between the classic pure analysis and the applied topics, including measure theory, Lebesgue Integral,harmonic analysis and wavelet theory with many associated applications.*The text is relatively elementary at the start, but the level of difficulty steadily increases*The book contains many clear, detailed examples, case studies and exercises*Many real world applications relating to
Mathematical analysis. --- Wavelets (Mathematics) --- Wavelet analysis --- 517.1 Mathematical analysis --- Mathematical analysis --- Harmonic analysis
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An in-depth look at real analysis and its applications, including an introduction to wavelet analysis, a popular topic in "applied real analysis". This text makes a very natural connection between the classic pure analysis and the applied topics, including measure theory, Lebesgue Integral, harmonic analysis and wavelet theory with many associated applications. *The text is relatively elementary at the start, but the level of difficulty steadily increases *The book contains many clear, detailed examples, case studies and exercises *Many real world applications relating to measure theory and pure analysis *Introduction to wavelet analysis.
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