Gaussian processes on graphs via spectral kernel learning
We propose a graph spectrum-based Gaussian process for prediction of signals defined on nodes of the graph. The model is designed to capture various graph signal structures through a highly adaptive kernel that incorporates a flexible polynomial function in the graph spectral domain. Unlike most exi...
| Main Authors: | , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
IEEE
2023
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