Time-Frequency Filtering Meets Graph Clustering
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866914082273099776 |
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| author | Colominas, Marcelo A. Steinerberger, Stefan Wu, Hau-Tieng |
| author_facet | Colominas, Marcelo A. Steinerberger, Stefan Wu, Hau-Tieng |
| contents | We show that the problem of identifying different signal components from a time-frequency representation can be equivalently phrased as a graph clustering problem: given a graph $G=(V,E)$ one aims to identify `clusters', subgraphs that are strongly connected and have relatively few connections between them. The graph clustering problem is well studied, we show how these ideas can suggest (many) new ways to identify signal components. Numerical experiments illustrate the ideas. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_07503 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Time-Frequency Filtering Meets Graph Clustering Colominas, Marcelo A. Steinerberger, Stefan Wu, Hau-Tieng Signal Processing Machine Learning We show that the problem of identifying different signal components from a time-frequency representation can be equivalently phrased as a graph clustering problem: given a graph $G=(V,E)$ one aims to identify `clusters', subgraphs that are strongly connected and have relatively few connections between them. The graph clustering problem is well studied, we show how these ideas can suggest (many) new ways to identify signal components. Numerical experiments illustrate the ideas. |
| title | Time-Frequency Filtering Meets Graph Clustering |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2510.07503 |