Kernel ridge regression for generalized graph signal processing
In generalized graph signal processing (GGSP), a function (an element from a separable Hilbert space) is associated with each vertex. To perform non-linear filtering and regression under the GGSP framework, we formulate an operator-valued kernel ridge regression (KRR) filtering approach. Under a spe...
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| Main Authors: | , |
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| Other Authors: | |
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/166434 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | In generalized graph signal processing (GGSP), a function (an element from a separable Hilbert space) is associated with each vertex. To perform non-linear filtering and regression under the GGSP framework, we formulate an operator-valued kernel ridge regression (KRR) filtering approach. Under a specific choice of separable kernels, we show that this problem is equivalent to learning a nonlinear frequency response on each frequency band. We specify the choice of the reproducing kernel according to the signal's spectral properties and discuss its effect on the learning result. The proposed approach is validated on a real dataset and demonstrated to outperform other competing methods. |
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