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Persistence Theory: From Quiver Representations to Data Analysis

Author: Steve Y. Oudot

Publisher: American Mathematical Soc.

Published: 2017-05-17

Total Pages: 218

ISBN-13: 1470434431

Persistence theory emerged in the early 2000s as a new theory in the area of applied and computational topology. This book provides a broad and modern view of the subject, including its algebraic, topological, and algorithmic aspects. It also elaborates on applications in data analysis. The level of detail of the exposition has been set so as to keep a survey style, while providing sufficient insights into the proofs so the reader can understand the mechanisms at work. The book is organized into three parts. The first part is dedicated to the foundations of persistence and emphasizes its connection to quiver representation theory. The second part focuses on its connection to applications through a few selected topics. The third part provides perspectives for both the theory and its applications. The book can be used as a text for a course on applied topology or data analysis.

The Structure and Stability of Persistence Modules

Author: Frédéric Chazal

Publisher: Springer

Published: 2016-10-08

Total Pages: 120

ISBN-13: 3319425455

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This book is a comprehensive treatment of the theory of persistence modules over the real line. It presents a set of mathematical tools to analyse the structure and to establish the stability of such modules, providing a sound mathematical framework for the study of persistence diagrams. Completely self-contained, this brief introduces the notion of persistence measure and makes extensive use of a new calculus of quiver representations to facilitate explicit computations. Appealing to both beginners and experts in the subject, The Structure and Stability of Persistence Modules provides a purely algebraic presentation of persistence, and thus complements the existing literature, which focuses mainly on topological and algorithmic aspects.

Topological Persistence in Geometry and Analysis

Author: Leonid Polterovich

Publisher: American Mathematical Soc.

Published: 2020-05-11

Total Pages: 128

ISBN-13: 1470454955

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The theory of persistence modules originated in topological data analysis and became an active area of research in algebraic topology. This book provides a concise and self-contained introduction to persistence modules and focuses on their interactions with pure mathematics, bringing the reader to the cutting edge of current research. In particular, the authors present applications of persistence to symplectic topology, including the geometry of symplectomorphism groups and embedding problems. Furthermore, they discuss topological function theory, which provides new insight into oscillation of functions. The book is accessible to readers with a basic background in algebraic and differential topology.

Computational Topology for Data Analysis

Author: Tamal Krishna Dey

Publisher: Cambridge University Press

Published: 2022-03-10

Total Pages: 456

ISBN-13: 1009103199

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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.

Elementary Applied Topology

Author: Robert W. Ghrist

Publisher: Createspace Independent Publishing Platform

Published: 2014

Total Pages: 0

ISBN-13: 9781502880857

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This book gives an introduction to the mathematics and applications comprising the new field of applied topology. The elements of this subject are surveyed in the context of applications drawn from the biological, economic, engineering, physical, and statistical sciences.

Geometric and Topological Inference

Author: Jean-Daniel Boissonnat

Publisher: Cambridge University Press

Published: 2018-09-27

Total Pages: 247

ISBN-13: 1108419399

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A rigorous introduction to geometric and topological inference, for anyone interested in a geometric approach to data science.

Computational Topology

Author: Herbert Edelsbrunner

Publisher: American Mathematical Society

Published: 2022-01-31

Total Pages: 241

ISBN-13: 1470467690

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Combining concepts from topology and algorithms, this book delivers what its title promises: an introduction to the field of computational topology. Starting with motivating problems in both mathematics and computer science and building up from classic topics in geometric and algebraic topology, the third part of the text advances to persistent homology. This point of view is critically important in turning a mostly theoretical field of mathematics into one that is relevant to a multitude of disciplines in the sciences and engineering. The main approach is the discovery of topology through algorithms. The book is ideal for teaching a graduate or advanced undergraduate course in computational topology, as it develops all the background of both the mathematical and algorithmic aspects of the subject from first principles. Thus the text could serve equally well in a course taught in a mathematics department or computer science department.

Algebraic Foundations for Applied Topology and Data Analysis

Author: Hal Schenck

Publisher: Springer Nature

Published: 2022-11-21

Total Pages: 231

ISBN-13: 3031066642

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This book gives an intuitive and hands-on introduction to Topological Data Analysis (TDA). Covering a wide range of topics at levels of sophistication varying from elementary (matrix algebra) to esoteric (Grothendieck spectral sequence), it offers a mirror of data science aimed at a general mathematical audience. The required algebraic background is developed in detail. The first third of the book reviews several core areas of mathematics, beginning with basic linear algebra and applications to data fitting and web search algorithms, followed by quick primers on algebra and topology. The middle third introduces algebraic topology, along with applications to sensor networks and voter ranking. The last third covers key contemporary tools in TDA: persistent and multiparameter persistent homology. Also included is a user’s guide to derived functors and spectral sequences (useful but somewhat technical tools which have recently found applications in TDA), and an appendix illustrating a number of software packages used in the field. Based on a course given as part of a masters degree in statistics, the book is appropriate for graduate students.

Topological Dynamics and Topological Data Analysis

Author: Robert L. Devaney

Publisher: Springer Nature

Published: 2021-09-23

Total Pages: 278

ISBN-13: 9811601747

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This book collects select papers presented at the International Workshop and Conference on Topology & Applications, held in Kochi, India, from 9–11 December 2018. The book discusses topics on topological dynamical systems and topological data analysis. Topics are ranging from general topology, algebraic topology, differential topology, fuzzy topology, topological dynamical systems, topological groups, linear dynamics, dynamics of operator network topology, iterated function systems and applications of topology. All contributing authors are eminent academicians, scientists, researchers and scholars in their respective fields, hailing from around the world. The book is a valuable resource for researchers, scientists and engineers from both academia and industry.

Computational Topology for Data Analysis

Author: Tamal Krishna Dey

Publisher: Cambridge University Press

Published: 2022-03-10

Total Pages: 455

ISBN-13: 1009098160

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This book provides a computational and algorithmic foundation for techniques in topological data analysis, with examples and exercises.