Computational topology for data analysis:
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 intr...
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| Hauptverfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2022
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| Schlagworte: | |
| Online-Zugang: | BSB01 FHN01 TUM01 UBG01 Volltext |
| Zusammenfassung: | 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. |
| Beschreibung: | Title from publisher's bibliographic system (viewed on 18 Feb 2022) |
| Beschreibung: | 1 Online-Ressource (xix, 433 Seiten) |
| ISBN: | 9781009099950 |
| DOI: | 10.1017/9781009099950 |
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Datensatz im Suchindex
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| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| discipline_str_mv | Mathematik |
| doi_str_mv | 10.1017/9781009099950 |
| format | Electronic eBook |
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| illustrated | Not Illustrated |
| index_date | 2024-07-03T19:34:33Z |
| indexdate | 2024-07-10T09:25:25Z |
| institution | BVB |
| isbn | 9781009099950 |
| language | English |
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| physical | 1 Online-Ressource (xix, 433 Seiten) |
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| publisher | Cambridge University Press |
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| spelling | Dey, Tamal 1964- (DE-588)138060134 aut Computational topology for data analysis Tamal Krishna Dey, Purdue University, Yusu Wang, University of California, San Diego Cambridge Cambridge University Press 2022 1 Online-Ressource (xix, 433 Seiten) txt rdacontent c rdamedia cr rdacarrier Title from publisher's bibliographic system (viewed on 18 Feb 2022) 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. Topology Wang, Yusu 1976- (DE-588)1253963703 aut Erscheint auch als Druck-Ausgabe 978-1-00-909816-8 https://doi.org/10.1017/9781009099950 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Dey, Tamal 1964- Wang, Yusu 1976- Computational topology for data analysis Topology |
| title | Computational topology for data analysis |
| title_auth | Computational topology for data analysis |
| title_exact_search | Computational topology for data analysis |
| title_exact_search_txtP | Computational topology for data analysis |
| title_full | Computational topology for data analysis Tamal Krishna Dey, Purdue University, Yusu Wang, University of California, San Diego |
| title_fullStr | Computational topology for data analysis Tamal Krishna Dey, Purdue University, Yusu Wang, University of California, San Diego |
| title_full_unstemmed | Computational topology for data analysis Tamal Krishna Dey, Purdue University, Yusu Wang, University of California, San Diego |
| title_short | Computational topology for data analysis |
| title_sort | computational topology for data analysis |
| topic | Topology |
| topic_facet | Topology |
| url | https://doi.org/10.1017/9781009099950 |
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