Kernel Based Reconstruction for Generalized Graph Signal Processing

Xingchao Jian, Wee Peng Tay, Yonina C. Eldar

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)2308-2322
Number of pages15
JournalIEEE Transactions on Signal Processing
StatePublished - 30 Apr 2024

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering


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