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＜電子ブック＞
Topological and Statistical Methods for Complex Data : Tackling Large-Scale, High-Dimensional, and Multivariate Data Spaces / edited by Janine Bennett, Fabien Vivodtzev, Valerio Pascucci
(Mathematics and Visualization. ISSN:2197666X)

版	1st ed. 2015.
出版者	(Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer)
出版年	2015
大きさ	XV, 297 p. 120 illus., 101 illus. in color : online resource
著者標目	Bennett, Janine editor Vivodtzev, Fabien editor Pascucci, Valerio editor SpringerLink (Online service)
件　名	LCSH:Topology LCSH:Statistics  LCSH:Mathematics LCSH:Algorithms LCSH:Information visualization LCSH:Manifolds (Mathematics) FREE:Topology FREE:Statistical Theory and Methods FREE:Applications of Mathematics FREE:Algorithms FREE:Data and Information Visualization FREE:Manifolds and Cell Complexes
一般注記	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 HTTP:URL=https://doi.org/10.1007/978-3-662-44900-4

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