학술논문
Learning Narrowband Graph Spectral Kernels for Graph Signal Estimation
Document Type
Conference
Author
Source
2022 30th Signal Processing and Communications Applications Conference (SIU) Signal Processing and Communications Applications Conference (SIU), 2022 30th. :1-4 May, 2022
Subject
Language
Abstract
In this work, we study the problem of estimating graph signals from incomplete observations. We propose a method that learns the spectrum of the graph signal collection at hand by fitting a set of narrowband graph kernels to the observed signal values. The unobserved graph signal values are then estimated using the sparse representations of the signals in the graph dictionary formed by the learnt kernels. Experimental results on graph data sets show that the proposed method compares favorably to baseline graph-based semi-supervised regression solutions.