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Computational topology for data analysis / Tamal Krishna Dey (Purdue University), Yusu Wang (University of California, San Diego)

By: Contributor(s): Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Publisher: Cambridge, United Kingdom ; New York, NY, USA ; Port Melbourne, VIC, Australia ; New Delhi, India ; Singapore : Cambridge University Press, 2022Description: 1 Online-Ressource (xix, 433 Seiten) : Illustrationen, DiagrammeISBN:
  • 9781009099950
Subject(s): Additional physical formats: 9781009098168. | Erscheint auch als: Computational topology for data analysis. Druck-Ausgabe Cambridge, United Kingdom : Cambridge University Press, 2022. xix, 433 SeitenMSC: MSC: 57-01 | 62R40 | 55N31 | 62-01RVK: RVK: SK 280DOI: DOI: 10.1017/9781009099950Online resources: Summary: 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.PPN: PPN: 1796040789Package identifier: Produktsigel: ZDB-20-CBO | ZDB-20-CTM | ZDB-20-CBM
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