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Topological and statistical methods for complex data[electronic resource] :tackling large-scale, high-dimensional, and multivariate data spaces /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	514
書名/作者:	Topological and statistical methods for complex data : tackling large-scale, high-dimensional, and multivariate data spaces // edited by Janine Bennett, Fabien Vivodtzev, Valerio Pascucci.
其他作者:	Bennett, Janine.
出版者:	Berlin, Heidelberg : : Springer Berlin Heidelberg :, 2015.
面頁冊數:	xv, 297 p. : : ill., digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Topology.
標題:	Mathematical analysis.
標題:	Visualization.
標題:	Mathematics.
標題:	Statistical Theory and Methods.
標題:	Applications of Mathematics.
標題:	Algorithms.
標題:	Manifolds and Cell Complexes (incl. Diff.Topology)
ISBN:	9783662449004 (electronic bk.)
ISBN:	9783662448991 (paper)
內容註:	I. Large-scale data analysis: In-situ and distributed analysis -- II. Large-scale data analysis: Efficient representation of large-functions -- III. Multi-variate data analysis: Structural techniques -- IV. Multi-variate data analysis: Classification and visualization of vector fields -- V. High-dimensional data analysis: Exploration of high-dimensional models -- VI. High-dimensional data analysis: Analysis of large systems.
摘要、提要註:	This book contains papers presented at the Workshop on the Analysis of Large-scale, High-Dimensional, and Multi-Variate Data Using Topology and Statistics, held in Le Barp, France, June 2013. It features the work of some of the most prominent and recognized leaders in the field who examine challenges as well as detail solutions to the analysis of extreme scale data. The book presents new methods that leverage the mutual strengths of both topological and statistical techniques to support the management, analysis, and visualization of complex data. It covers both theory and application and provides readers with an overview of important key concepts and the latest research trends. Coverage in the book includes multi-variate and/or high-dimensional analysis techniques, feature-based statistical methods, combinatorial algorithms, scalable statistics algorithms, scalar and vector field topology, and multi-scale representations. In addition, the book details algorithms that are broadly applicable and can be used by application scientists to glean insight from a wide range of complex data sets.
電子資源:	http://dx.doi.org/10.1007/978-3-662-44900-4

Topological and statistical methods for complex data[electronic resource] :tackling large-scale, high-dimensional, and multivariate data spaces /
Topological and statistical methods for complex datatackling large-scale, high-dimensional, and multivariate data spaces /[electronic resource] :edited by Janine Bennett, Fabien Vivodtzev, Valerio Pascucci. - Berlin, Heidelberg :Springer Berlin Heidelberg :2015. - xv, 297 p. :ill., digital ;24 cm. - Mathematics and visualization,1612-3786. - Mathematics and visualization..

I. Large-scale data analysis: In-situ and distributed analysis -- II. Large-scale data analysis: Efficient representation of large-functions -- III. Multi-variate data analysis: Structural techniques -- IV. Multi-variate data analysis: Classification and visualization of vector fields -- V. High-dimensional data analysis: Exploration of high-dimensional models -- VI. High-dimensional data analysis: Analysis of large systems.

This book contains papers presented at the Workshop on the Analysis of Large-scale, High-Dimensional, and Multi-Variate Data Using Topology and Statistics, held in Le Barp, France, June 2013. It features the work of some of the most prominent and recognized leaders in the field who examine challenges as well as detail solutions to the analysis of extreme scale data. The book presents new methods that leverage the mutual strengths of both topological and statistical techniques to support the management, analysis, and visualization of complex data. It covers both theory and application and provides readers with an overview of important key concepts and the latest research trends. Coverage in the book includes multi-variate and/or high-dimensional analysis techniques, feature-based statistical methods, combinatorial algorithms, scalable statistics algorithms, scalar and vector field topology, and multi-scale representations. In addition, the book details algorithms that are broadly applicable and can be used by application scientists to glean insight from a wide range of complex data sets.

ISBN: 9783662449004 (electronic bk.)

Standard No.: 10.1007/978-3-662-44900-4doiSubjects--Topical Terms:

405711
Topology.

LC Class. No.: QA611

Dewey Class. No.: 514

Topological and statistical methods for complex data[electronic resource] :tackling large-scale, high-dimensional, and multivariate data spaces /
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