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- W4385849062 abstract "In generalized graph signal processing (GGSP), the signal associated with each vertex in a graph is an element from a Hilbert space. In this paper, we study GGSP signal reconstruction as a kernel ridge regression (KRR) problem. By devising an appropriate kernel, we show that this problem has a solution that can be evaluated in a distributed way. We interpret the problem and solution using both deterministic and Bayesian perspectives and link them to existing graph signal processing and GGSP frameworks. We then provide an online implementation via random Fourier features. Under the Bayesian framework, we investigate the statistical performance under the asymptotic sampling scheme. Finally, we validate our theory and methods on real-world datasets." @default.
- W4385849062 created "2023-08-16" @default.
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- W4385849062 date "2023-08-14" @default.
- W4385849062 modified "2023-09-27" @default.
- W4385849062 title "Kernel Based Reconstruction for Generalized Graph Signal Processing" @default.
- W4385849062 doi "https://doi.org/10.48550/arxiv.2308.06949" @default.
- W4385849062 hasPublicationYear "2023" @default.
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