Geometric Structure of High-Dimensional Data and Dimensionality Reduction [recurso electrónico] / by Jianzhong Wang.
Tipo de material:
TextoEditor: Berlin, Heidelberg : Springer Berlin Heidelberg, 2011Descripción: Approx. 365 p. 91 illus. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9783642274978Tema(s): Computer science | Data structures (Computer science) | Data mining | Mathematics | Computer Science | Data Mining and Knowledge Discovery | Probability and Statistics in Computer Science | Applications of Mathematics | Data Structures, Cryptology and Information TheoryFormatos físicos adicionales: Printed edition:: Sin títuloClasificación CDD: 006.312 Clasificación LoC:QA76.9.D343Recursos en línea: Libro electrónico
En: Springer eBooksResumen: "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.
| Tipo de ítem | Biblioteca actual | Colección | Signatura | Copia número | Estado | Fecha de vencimiento | Código de barras |
|---|---|---|---|---|---|---|---|
| Libro Electrónico | Biblioteca Electrónica | Colección de Libros Electrónicos | QA76.9 .D343 (Browse shelf(Abre debajo)) | 1 | No para préstamo | 376949-2001 |
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| QA76.9 .D343 Journal on Data Semantics XV | QA76.9 .D343 Advances in Data Mining. Applications and Theoretical Aspects | QA76.9 .D343 Knowledge Science, Engineering and Management | QA76.9 .D343 Geometric Structure of High-Dimensional Data and Dimensionality Reduction | QA76.9 .D343 Advances in Machine Learning and Data Analysis | QA76.9 .D343 Affective Computing and Sentiment Analysis | QA76.9 .D35 Pro Entity Framework 4.0 |
"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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