Data analysis using discrete cubical homology
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866915352022089728 |
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| author | Kapulkin, Chris Kershaw, Nathan |
| author_facet | Kapulkin, Chris Kershaw, Nathan |
| contents | We present a new tool for data analysis: persistence discrete homology, which is well-suited to analyze filtrations of graphs. In particular, we provide a novel way of representing high-dimensional data as a filtration of graphs using pairwise correlations. We discuss several applications of these tools, e.g., in weather and financial data, comparing them to the standard methods used in the respective fields. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_15020 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data analysis using discrete cubical homology Kapulkin, Chris Kershaw, Nathan Algebraic Topology Machine Learning Combinatorics Statistics Theory 62R40, 68T09, 05C90, 55U05 We present a new tool for data analysis: persistence discrete homology, which is well-suited to analyze filtrations of graphs. In particular, we provide a novel way of representing high-dimensional data as a filtration of graphs using pairwise correlations. We discuss several applications of these tools, e.g., in weather and financial data, comparing them to the standard methods used in the respective fields. |
| title | Data analysis using discrete cubical homology |
| topic | Algebraic Topology Machine Learning Combinatorics Statistics Theory 62R40, 68T09, 05C90, 55U05 |
| url | https://arxiv.org/abs/2506.15020 |