On the Bootstrap for Persistence Diagrams and Landscapes
Persistent homology probes topological properties from point clouds and functions. By looking at multiple scales simultaneously, one can record the births and deaths of topological features as the scale varies. In this paper we use a statistical technique, the empirical bootstrap, to separate topolo...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Yaroslavl State University
2013-12-01
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| Series: | Моделирование и анализ информационных систем |
| Subjects: | |
| Online Access: | https://www.mais-journal.ru/jour/article/view/162 |
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| Summary: | Persistent homology probes topological properties from point clouds and functions. By looking at multiple scales simultaneously, one can record the births and deaths of topological features as the scale varies. In this paper we use a statistical technique, the empirical bootstrap, to separate topological signal from topological noise. In particular, we derive confidence sets for persistence diagrams and confi- dence bands for persistence landscapes.The article is published in the author’s wording. |
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| ISSN: | 1818-1015 2313-5417 |