Kernel ridge regression for generalized graph signal processing

• Main Authors: Jian, Xingchao, Tay, Wee Peng
• 其他作者: School of Electrical and Electronic Engineering
• 格式: Conference or Workshop Item
• 語言: English
• 出版: 2023
• 主題: Engineering::Electrical and electronic engineering; Graph Signal Processing; Hilbert Space
• 在線閱讀: https://hdl.handle.net/10356/166434
• 機構: Nanyang Technological University

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.