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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2407.11429 |
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| _version_ | 1866917723179581440 |
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| author | Batreddy, Subbareddy Mishra, Pushkal Kakarla, Yaswanth Siripuram, Aditya |
| author_facet | Batreddy, Subbareddy Mishra, Pushkal Kakarla, Yaswanth Siripuram, Aditya |
| contents | Given partial measurements of a time-varying graph signal, we propose an algorithm to simultaneously estimate both the underlying graph topology and the missing measurements. The proposed algorithm operates by training an interpretable neural network, designed from the unrolling framework. The proposed technique can be used both as a graph learning and a graph signal reconstruction algorithm. This work enhances prior work in graph signal reconstruction by allowing the underlying graph to be unknown; and also builds on prior work in graph learning by tailoring the learned graph to the signal reconstruction task. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_11429 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Joint Data Inpainting and Graph Learning via Unrolled Neural Networks Batreddy, Subbareddy Mishra, Pushkal Kakarla, Yaswanth Siripuram, Aditya Signal Processing Machine Learning Given partial measurements of a time-varying graph signal, we propose an algorithm to simultaneously estimate both the underlying graph topology and the missing measurements. The proposed algorithm operates by training an interpretable neural network, designed from the unrolling framework. The proposed technique can be used both as a graph learning and a graph signal reconstruction algorithm. This work enhances prior work in graph signal reconstruction by allowing the underlying graph to be unknown; and also builds on prior work in graph learning by tailoring the learned graph to the signal reconstruction task. |
| title | Joint Data Inpainting and Graph Learning via Unrolled Neural Networks |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2407.11429 |