Ordinal spaces
Fuente:
arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866916539729444864 |
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| author | Keller, Karsten Petrov, Evgeniy |
| author_facet | Keller, Karsten Petrov, Evgeniy |
| contents | Ordinal data analysis is an interesting direction in machine learning. It mainly deals with data for which only the relationships `$<$', `$=$', `$>$' between pairs of points are known. We do an attempt of formalizing structures behind ordinal data analysis by introducing the notion of ordinal spaces on the base of a strict axiomatic approach. For these spaces we study general properties as isomorphism conditions, connections with metric spaces, embeddability in Euclidean spaces, topological properties etc. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_17391 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Ordinal spaces Keller, Karsten Petrov, Evgeniy General Topology Primary 54E99, Secondary 54E25, 54E35 Ordinal data analysis is an interesting direction in machine learning. It mainly deals with data for which only the relationships `$<$', `$=$', `$>$' between pairs of points are known. We do an attempt of formalizing structures behind ordinal data analysis by introducing the notion of ordinal spaces on the base of a strict axiomatic approach. For these spaces we study general properties as isomorphism conditions, connections with metric spaces, embeddability in Euclidean spaces, topological properties etc. |
| title | Ordinal spaces |
| topic | General Topology Primary 54E99, Secondary 54E25, 54E35 |
| url | https://arxiv.org/abs/2412.17391 |