A Hilbert space theory of generalized graph signal processing

A Hilbert space theory of generalized graph signal processing

Graph signal processing (GSP) has become an important tool in many areas such as image processing, networking learning and analysis of social network data. In this paper, we propose a broader framework that not only encompasses traditional GSP as a special case, but also includes a hybrid framewo...

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Bibliographic Details
Main Authors: Ji, Feng, Tay, Wee Peng
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2021
Subjects:
Engineering::Electrical and electronic engineering
Graph Signal Proceesing
Hilbert Space
Online Access:https://hdl.handle.net/10356/154495
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Institution: Nanyang Technological University
Language: English
Description
Summary:Graph signal processing (GSP) has become an important tool in many areas such as image processing, networking learning and analysis of social network data. In this paper, we propose a broader framework that not only encompasses traditional GSP as a special case, but also includes a hybrid framework of graph and classical signal processing over a continuous domain. Our framework relies extensively on concepts and tools from functional analysis to generalize traditional GSP to graph signals in a separable Hilbert space with infinite dimensions. We develop a concept analogous to Fourier transform for generalized GSP and the theory of filtering and sampling such signals.

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