Cargando…
Geometric Structure of High-Dimensional Data and Dimensionality Reduction
"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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2012.
|
| Edición: | 1st ed. 2012. |
| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
| LEADER | 00000nam a22000005i 4500 | ||
|---|---|---|---|
| 001 | 978-3-642-27497-8 | ||
| 003 | DE-He213 | ||
| 005 | 20220116230226.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 120427s2012 gw | s |||| 0|eng d | ||
| 020 | |a 9783642274978 |9 978-3-642-27497-8 | ||
| 024 | 7 | |a 10.1007/978-3-642-27497-8 |2 doi | |
| 050 | 4 | |a QA76.9.D343 | |
| 072 | 7 | |a UNF |2 bicssc | |
| 072 | 7 | |a UYQE |2 bicssc | |
| 072 | 7 | |a COM021030 |2 bisacsh | |
| 072 | 7 | |a UNF |2 thema | |
| 072 | 7 | |a UYQE |2 thema | |
| 082 | 0 | 4 | |a 006.312 |2 23 |
| 100 | 1 | |a Wang, Jianzhong. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Geometric Structure of High-Dimensional Data and Dimensionality Reduction |h [electronic resource] / |c by Jianzhong Wang. |
| 250 | |a 1st ed. 2012. | ||
| 264 | 1 | |a Berlin, Heidelberg : |b Springer Berlin Heidelberg : |b Imprint: Springer, |c 2012. | |
| 300 | |a XVIII, 356 p. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file |b PDF |2 rda | ||
| 520 | |a "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. | ||
| 650 | 0 | |a Data mining. | |
| 650 | 0 | |a Computer science-Mathematics. | |
| 650 | 0 | |a Mathematical statistics. | |
| 650 | 0 | |a Mathematics. | |
| 650 | 0 | |a Data structures (Computer science). | |
| 650 | 0 | |a Information theory. | |
| 650 | 1 | 4 | |a Data Mining and Knowledge Discovery. |
| 650 | 2 | 4 | |a Probability and Statistics in Computer Science. |
| 650 | 2 | 4 | |a Applications of Mathematics. |
| 650 | 2 | 4 | |a Data Structures and Information Theory. |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783642274985 |
| 776 | 0 | 8 | |i Printed edition: |z 9783642274961 |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-3-642-27497-8 |z Texto Completo |
| 912 | |a ZDB-2-SCS | ||
| 912 | |a ZDB-2-SXCS | ||
| 950 | |a Computer Science (SpringerNature-11645) | ||
| 950 | |a Computer Science (R0) (SpringerNature-43710) | ||