Introducing Graph Learning over Polytopic Uncertain Graph
Fuente:
arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866916202612260864 |
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| author | Kishida, Masako Ono, Shunsuke |
| author_facet | Kishida, Masako Ono, Shunsuke |
| contents | This extended abstract introduces a class of graph learning applicable to cases where the underlying graph has polytopic uncertainty, i.e., the graph is not exactly known, but its parameters or properties vary within a known range. By incorporating this assumption that the graph lies in a polytopic set into two established graph learning frameworks, we find that our approach yields better results with less computation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_08176 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Introducing Graph Learning over Polytopic Uncertain Graph Kishida, Masako Ono, Shunsuke Signal Processing Machine Learning This extended abstract introduces a class of graph learning applicable to cases where the underlying graph has polytopic uncertainty, i.e., the graph is not exactly known, but its parameters or properties vary within a known range. By incorporating this assumption that the graph lies in a polytopic set into two established graph learning frameworks, we find that our approach yields better results with less computation. |
| title | Introducing Graph Learning over Polytopic Uncertain Graph |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2404.08176 |