Chromatic Topological Data Analysis
Fuente:
arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | |
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| _version_ | 1866910475133911040 |
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| author | di Montesano, Sebastiano Cultrera Draganov, Ondrej Edelsbrunner, Herbert Saghafian, Morteza |
| author_facet | di Montesano, Sebastiano Cultrera Draganov, Ondrej Edelsbrunner, Herbert Saghafian, Morteza |
| contents | Exploring the shape of point configurations has been a key driver in the evolution of TDA (short for topological data analysis) since its infancy. This survey illustrates the recent efforts to broaden these ideas to model spatial interactions among multiple configurations, each distinguished by a color. It describes advances in this area and prepares the ground for further exploration by mentioning unresolved questions and promising research avenues while focusing on the overlap with discrete geometry. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_04102 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Chromatic Topological Data Analysis di Montesano, Sebastiano Cultrera Draganov, Ondrej Edelsbrunner, Herbert Saghafian, Morteza Computational Geometry Exploring the shape of point configurations has been a key driver in the evolution of TDA (short for topological data analysis) since its infancy. This survey illustrates the recent efforts to broaden these ideas to model spatial interactions among multiple configurations, each distinguished by a color. It describes advances in this area and prepares the ground for further exploration by mentioning unresolved questions and promising research avenues while focusing on the overlap with discrete geometry. |
| title | Chromatic Topological Data Analysis |
| topic | Computational Geometry |
| url | https://arxiv.org/abs/2406.04102 |