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Discrete Linear Canonical Transform on Graphs: Fast Sampling Set Selection Method

Published: 08 June 2024 Publication History

Abstract

With the flourishing development of graph signal processing, an increasing number of classical signal processing methods are being incorporated into this field, and the graph linear canonical transform (GLCT) is one such example. In this paper, we address the problem of signal sampling set selection in the GLCT domain based on the proposed GLCT sampling theory. We present a novel fast sampling method. Furthermore, we discuss the relationship between the proposed method and existing sampling set selection methods based on the GLCT spectrum. It is demonstrated that the proposed method considers GLCT spectrum information without the need for the eigendecomposition of the variation operator. Finally, the performance of the proposed method was validated through the selection of vertices, comparing results in terms of reconstruction error and recovery time, which demonstrated its superior efficacy.

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  • (2024)Discrete linear canonical transform on graphs: Uncertainty principle and samplingSignal Processing10.1016/j.sigpro.2024.109668(109668)Online publication date: Aug-2024

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IVSP '24: Proceedings of the 2024 6th International Conference on Image, Video and Signal Processing
March 2024
229 pages
ISBN:9798400716829
DOI:10.1145/3655755
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 08 June 2024

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  1. Graph signal processing
  2. fast sampling algorithm
  3. graph linear canonical transform
  4. sampling set selection

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  • (2024)Discrete linear canonical transform on graphs: Uncertainty principle and samplingSignal Processing10.1016/j.sigpro.2024.109668(109668)Online publication date: Aug-2024

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