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|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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