Functional and shape data analysis / Anuj Srivastava and Eric P. Klassen.
Material type:
TextSeries: Springer series in statisticsPublication details: New York : Springer-Verlag, 2016.Description: xviii, 447 pages : illustrations (some color) ; 26 cmISBN: - 9781493940189
- 515.7 23 Sr774
| Item type | Current library | Call number | Status | Date due | Barcode | Item holds | |
|---|---|---|---|---|---|---|---|
| Books | ISI Library, Kolkata | 515.7 Sr774 (Browse shelf(Opens below)) | Available | 137681 |
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| 515.7 Si617 On fixed point theorems | 515.7 Si618 Introduction to the methods of real analysis | 515.7 Sm635 Functional equations and how to solve them | 515.7 Sr774 Functional and shape data analysis / | 515.7 St819 Functional analysis | 515.7 Su947 Functional analysis | 515.7 Su957 Functional analysis |
Includes bibliographical references and index.
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 --
Background material --
The dynamic programming algorithm.
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. Covering a broad range of ideas from different disciplines, it is aimed at graduate students in analysis in statistics, engineering, applied mathematics, neuroscience, biology, bioinformatics, and other related areas. 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.
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