國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Functional and shape data analysis

Klassen, Eric P.

Functional and shape data analysis

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Functional and shape data analysisby Anuj Srivastava, Eric P. Klassen.
作者:	Srivastava, Anuj.
其他作者:	Klassen, Eric P.
出版者:	New York, NY :Springer New York :2016.
面頁冊數:	xviii, 447 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
標題:	Functional analysis.
電子資源:	http://dx.doi.org/10.1007/978-1-4939-4020-2
ISBN:	9781493940202$q(electronic bk.)

Functional and shape data analysis
Srivastava, Anuj.

Functional and shape data analysis[electronic resource] /by Anuj Srivastava, Eric P. Klassen. - New York, NY :Springer New York :2016. - xviii, 447 p. :ill. (some col.), digital ;24 cm. - Springer series in statistics,0172-7397. - Springer series in statistics..

1. Motivation for Function and Shape Analysis -- 2. Previous Techniques in Shape Analysis -- 3. Background: Relevant Tools from Geometry -- 4. Functional Data and Elastic Registration -- 5. Shapes of Planar Curves -- 6. Shapes of Planar Closed Curves -- 7. Statistical Modeling on Nonlinear Manifolds -- 8. Statistical Modeling of Functional Data -- 9. Statistical Modeling of Planar Shapes -- 10. Shapes of Curves in Higher Dimensions -- 11. Related Topics in Shape Analysis of Curves -- A. Background Material -- B. The Dynamic Programming Algorithm -- References -- Index.

This textbook for courses on function data analysis and shape data analysis describes how to define, compare, and mathematically represent shapes, with a focus on statistical modeling and inference. It is aimed at graduate students in analysis in statistics, engineering, applied mathematics, neuroscience, biology, bioinformatics, and other related areas. The interdisciplinary nature of the broad range of ideas covered--from introductory theory to algorithmic implementations and some statistical case studies--is meant to familiarize graduate students with an array of tools that are relevant in developing computational solutions for shape and related analyses. These tools, gleaned from geometry, algebra, statistics, and computational science, are traditionally scattered across different courses, departments, and disciplines; Functional and Shape Data Analysis offers a unified, comprehensive solution by integrating the registration problem into shape analysis, better preparing graduate students for handling future scientific challenges. Recently, a data-driven and application-oriented focus on shape analysis has been trending. This text offers a self-contained treatment of this new generation of methods in shape analysis of curves. Its main focus is shape analysis of functions and curves--in one, two, and higher dimensions--both closed and open. It develops elegant Riemannian frameworks that provide both quantification of shape differences and registration of curves at the same time. Additionally, these methods are used for statistically summarizing given curve data, performing dimension reduction, and modeling observed variability. It is recommended that the reader have a background in calculus, linear algebra, numerical analysis, and computation. Presents a complete and detailed exposition on statistical analysis of shapes that includes appendices, background material, and exercises, making this text a self-contained reference Addresses and explores the next generation of shape analysis Focuses on providing a working knowledge of a broad range of relevant material, foregoing in-depth technical details and elaborate mathematical explanations Anuj Srivastava is a Professor in the Department of Statistics and a Distinguished Research Professor at Florida State University. His areas of interest include statistical analysis on nonlinear manifolds, statistical computer vision, functional data analysis, and statistical shape theory. He has been the associate editor for the Journal of Statistical Planning and Inference, and several IEEE journals. He is a fellow of the International Association of Pattern Recognition(IAPR) and a senior member of the Institute for Electrical and Electronic Engineers (IEEE) Eric Klassen is a Professor in the Department of Mathematics at Florida State University. His mathematical interests include topology, geometry, and shape analysis. In his spare time, he enjoys playing the piano, riding his bike, and contra dancing.

ISBN: 9781493940202$q(electronic bk.)

Standard No.: 10.1007/978-1-4939-4020-2doiSubjects--Topical Terms:

200585
Functional analysis.

LC Class. No.: QA320

Dewey Class. No.: 515.7

Functional and shape data analysis
LDR:04577nmm a2200325 a 4500 001 498425
003 DE-He213
005 20161003093948.0
006 m d
007 cr nn 008maaau
008 170511s2016 nyu s 0 eng d
020 $a 9781493940202$q(electronic bk.)
020 $a 9781493940189$q(paper)
024 7 $a 10.1007/978-1-4939-4020-2 $2 doi
035 $a 978-1-4939-4020-2
040 $a GP $c GP
041 0 $a eng
050 4 $a QA320
072 7 $a PBT $2 bicssc
072 7 $a MAT029000 $2 bisacsh
082 0 4 $a 515.7 $2 23
090 $a QA320 $b .S774 2016
100 1 $a Srivastava, Anuj. $3 737740
245 1 0 $a Functional and shape data analysis $h [electronic resource] / $c by Anuj Srivastava, Eric P. Klassen.
260 $a New York, NY : $b Springer New York : $b Imprint: Springer, $c 2016.
300 $a xviii, 447 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Springer series in statistics, $x 0172-7397
505 0 $a 1. Motivation for Function and Shape Analysis -- 2. Previous Techniques in Shape Analysis -- 3. Background: Relevant Tools from Geometry -- 4. Functional Data and Elastic Registration -- 5. Shapes of Planar Curves -- 6. Shapes of Planar Closed Curves -- 7. Statistical Modeling on Nonlinear Manifolds -- 8. Statistical Modeling of Functional Data -- 9. Statistical Modeling of Planar Shapes -- 10. Shapes of Curves in Higher Dimensions -- 11. Related Topics in Shape Analysis of Curves -- A. Background Material -- B. The Dynamic Programming Algorithm -- References -- Index.
520 $a This textbook for courses on function data analysis and shape data analysis describes how to define, compare, and mathematically represent shapes, with a focus on statistical modeling and inference. It is aimed at graduate students in analysis in statistics, engineering, applied mathematics, neuroscience, biology, bioinformatics, and other related areas. The interdisciplinary nature of the broad range of ideas covered--from introductory theory to algorithmic implementations and some statistical case studies--is meant to familiarize graduate students with an array of tools that are relevant in developing computational solutions for shape and related analyses. These tools, gleaned from geometry, algebra, statistics, and computational science, are traditionally scattered across different courses, departments, and disciplines; Functional and Shape Data Analysis offers a unified, comprehensive solution by integrating the registration problem into shape analysis, better preparing graduate students for handling future scientific challenges. Recently, a data-driven and application-oriented focus on shape analysis has been trending. This text offers a self-contained treatment of this new generation of methods in shape analysis of curves. Its main focus is shape analysis of functions and curves--in one, two, and higher dimensions--both closed and open. It develops elegant Riemannian frameworks that provide both quantification of shape differences and registration of curves at the same time. Additionally, these methods are used for statistically summarizing given curve data, performing dimension reduction, and modeling observed variability. It is recommended that the reader have a background in calculus, linear algebra, numerical analysis, and computation. Presents a complete and detailed exposition on statistical analysis of shapes that includes appendices, background material, and exercises, making this text a self-contained reference Addresses and explores the next generation of shape analysis Focuses on providing a working knowledge of a broad range of relevant material, foregoing in-depth technical details and elaborate mathematical explanations Anuj Srivastava is a Professor in the Department of Statistics and a Distinguished Research Professor at Florida State University. His areas of interest include statistical analysis on nonlinear manifolds, statistical computer vision, functional data analysis, and statistical shape theory. He has been the associate editor for the Journal of Statistical Planning and Inference, and several IEEE journals. He is a fellow of the International Association of Pattern Recognition(IAPR) and a senior member of the Institute for Electrical and Electronic Engineers (IEEE) Eric Klassen is a Professor in the Department of Mathematics at Florida State University. His mathematical interests include topology, geometry, and shape analysis. In his spare time, he enjoys playing the piano, riding his bike, and contra dancing.
650 0 $a Functional analysis. $3 200585
650 1 4 $a Statistics. $3 182057
650 2 4 $a Statistical Theory and Methods. $3 274054
650 2 4 $a Functional Analysis. $3 274845
650 2 4 $a Geometry. $3 183883
650 2 4 $a Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. $3 348605
700 1 $a Klassen, Eric P. $3 761559
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a Springer series in statistics. $3 280826
856 4 0 $u http://dx.doi.org/10.1007/978-1-4939-4020-2
950 $a Mathematics and Statistics (Springer-11649)