On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912585561931776 |
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| author | Sripathmanathan, Baskaran Dong, Xiaowen Bronstein, Michael |
| author_facet | Sripathmanathan, Baskaran Dong, Xiaowen Bronstein, Michael |
| contents | We investigate graph signal reconstruction and sample selection for classification tasks. We present general theoretical characterisations of classification error applicable to multiple commonly used reconstruction methods, and compare that to the classical reconstruction error. We demonstrate the applicability of our results by using them to derive new optimal sampling methods for linearized graph convolutional networks, and show improvement over other graph signal processing based methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_10874 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals Sripathmanathan, Baskaran Dong, Xiaowen Bronstein, Michael Signal Processing Machine Learning We investigate graph signal reconstruction and sample selection for classification tasks. We present general theoretical characterisations of classification error applicable to multiple commonly used reconstruction methods, and compare that to the classical reconstruction error. We demonstrate the applicability of our results by using them to derive new optimal sampling methods for linearized graph convolutional networks, and show improvement over other graph signal processing based methods. |
| title | On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2509.10874 |