Adaptive Graph Normalized Sign Algorithm
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914787130081280 |
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| author | Peng, Changran Yan, Yi Kuruoglu, Ercan E. |
| author_facet | Peng, Changran Yan, Yi Kuruoglu, Ercan E. |
| contents | Efficient and robust prediction of graph signals is challenging when the signals are under impulsive noise and have missing data. Exploiting graph signal processing (GSP) and leveraging the simplicity of the classical adaptive sign algorithm, we propose an adaptive algorithm on graphs named the Graph Normalized Sign (GNS). GNS approximated a normalization term into the update, therefore achieving faster convergence and lower error compared to previous adaptive GSP algorithms. In the task of the online prediction of multivariate temperature data under impulsive noise, GNS outputs fast and robust predictions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_04107 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Adaptive Graph Normalized Sign Algorithm Peng, Changran Yan, Yi Kuruoglu, Ercan E. Signal Processing Efficient and robust prediction of graph signals is challenging when the signals are under impulsive noise and have missing data. Exploiting graph signal processing (GSP) and leveraging the simplicity of the classical adaptive sign algorithm, we propose an adaptive algorithm on graphs named the Graph Normalized Sign (GNS). GNS approximated a normalization term into the update, therefore achieving faster convergence and lower error compared to previous adaptive GSP algorithms. In the task of the online prediction of multivariate temperature data under impulsive noise, GNS outputs fast and robust predictions. |
| title | Adaptive Graph Normalized Sign Algorithm |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2405.04107 |