Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/154495
Title: A Hilbert space theory of generalized graph signal processing
Authors: Ji, Feng
Tay, Wee Peng
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2019
Source: Ji, F. & Tay, W. P. (2019). A Hilbert space theory of generalized graph signal processing. IEEE Transactions On Signal Processing, 67(24), 6188-6203. https://dx.doi.org/10.1109/TSP.2019.2952055
Project: MOE2018-T2-2-019
Journal: IEEE Transactions on Signal Processing
Abstract: 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.
URI: https://hdl.handle.net/10356/154495
ISSN: 1053-587X
DOI: 10.1109/TSP.2019.2952055
Rights: © 2019 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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