Topological data analysis (TDA) has emerged recently as a viable tool for analyzing complex data, and the area has grown substantially both in its methodologies and applicability. Providing a computational and algorithmic foundation for techniques in TDA, this comprehensive, self-contained text introduces students and researchers in mathematics and computer science to the current state of the field. The book features a description of mathematical objects and constructs behind recent advances, the algorithms involved, computational considerations, as well as examples of topological structures or ideas that can be used in applications. It provides a thorough treatment of persistent homology together with various extensions - like zigzag persistence and multiparameter persistence - and their applications to different types of data, like point clouds, triangulations, or graph data. Other important topics covered include discrete Morse theory, the Mapper structure, optimal generating cycles, as well as recent advances in embedding TDA within machine learning frameworks.
Rezensionen / Stimmen
'A must-have up-to-date computational account of a vibrant area connecting pure mathematics with applications.' Herbert Edelsbrunner, IST Austria 'This book provides a comprehensive treatment of the algorithmic aspects of topological persistence theory, both in the classical one-parameter setting and in the emerging multi-parameter setting. It is an excellent resource for practitioners within or outside the field, who want to learn about the current state-of-the-art algorithms in topological data analysis.' Steve Oudot, Inria and Ecole polytechnique 'There are many things to appreciate about this book, including the abundance of excellent and helpful figures, the extensive reference list, and the variety of instructive exercises for students to work through ... Thanks to its inclusion of so much cutting-edge recent work and state-of-the-art algorithms, this is an ideal book for mathematicians or computer scientists looking to dive into this exciting and still very young area of research.' Ellen Gasparovic, Mathematical Association of America Reviews 'This is a much needed update and contribution to the vast and rapidly growing area of computational topology. It will be the new go-to text for years to come.' Nicholas A. Scoville 'As a complete package, the book is an ideal text for a first course on topological data analysis for beginning graduate students ... Highly recommended.' M. Clay, Choice 'The work under consideration provides a thorough and comprehensive introduction to computational topology. The book is largely self-contained and does not presuppose prior knowledge of topology.' Walter D. Freyn, MathSciNet
Sprache
Verlagsort
Zielgruppe
Für höhere Schule und Studium
Illustrationen
Worked examples or Exercises
Maße
Höhe: 235 mm
Breite: 157 mm
Dicke: 29 mm
Gewicht
ISBN-13
978-1-009-09816-8 (9781009098168)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
Tamal Krishna Dey is Professor of Computer Science at Purdue University. Before joining Purdue, he was a faculty in the CSE department of The Ohio State University. He has held academic positions at Indiana University-Purdue University at Indianapolis, Indian Institute of Technology Kharagpur, and Max Planck Institute. His research interests include computational geometry, computational topology and their applications to geometric modeling and data analysis. He has (co)authored two books Curve and Surface Reconstruction: Algorithms with Mathematical Analysis (Cambridge University Press) and Delaunay Mesh Generation (CRC Press), and (co)authored more than 200 scientific articles. Dey is a fellow of the IEEE, ACM, and Solid Modeling Association. Yusu Wang is Professor in the Haliciouglu Data Science Institute at University of California, San Diego. Prior to joining UCSD, she was Professor of Computer Science and Engineering at the Ohio State University and post-doctoral fellow at Stanford University. Yusu primarily works in topological and geometric data analysis, developing effective and theoretically justified algorithms for data analysis using geometric and topological ideas, as well as in applying them to practical domains. She received the DOE Early Career Principal Investigator Award in 2006 and NSF Career Award in 2008.
Autor*in
Purdue University, Indiana
University of California, San Diego
1. Basics; 2. Complexes and homology groups; 3. Topological persistence; 4. General persistence; 5. Generators and optimality; 6. Topological analysis of point clouds; 7. Reeb graphs; 8. Topological analysis of graphs; 9. Cover, nerve and Mapper; 10. Discrete Morse theory and applications; 11. Multiparameter persistence and decomposition; 12. Multiparameter persistence and distances; 13. Topological persistence and machine learning.
