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Defining Graph Signal Distances Using an Optimal Mass Transport Framework

Juhlin, Maria LU ; Elvander, Filip LU and Jakobsson, Andreas LU orcid (2019) 27th European Signal Processing Conference (EUSIPCO) 2019.
Abstract
In this work, we propose a novel measure of distance for quantifying dissimilarities between signals observed on a graph. Building on a recently introduced optimal mass transport framework, the distance measure is formed using the second-order statistics of the graph signals, allowing for comparison of graph processes without direct access to the signals themselves, while explicitly taking the dynamics of the underlying graph into account. The behavior of the proposed distance notion is illustrated in a graph signal classification scenario, indicating attractive modeling properties as compared to the standard Euclidean metric.
Please use this url to cite or link to this publication:
author
; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
2019 27th European Signal Processing Conference, EUSIPCO 2019
volume
2019
pages
5 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
27th European Signal Processing Conference (EUSIPCO)
conference location
A Coruna, Spain
conference dates
2019-09-02 - 2019-09-06
external identifiers
  • scopus:85075607645
ISBN
978-1-5386-7300-3
978-9-0827-9703-9
DOI
10.23919/EUSIPCO.2019.8902502
language
English
LU publication?
yes
id
bd50d29e-e03b-44bc-90f8-eac4b7cb4c13
date added to LUP
2019-06-10 09:14:28
date last changed
2024-10-02 03:45:46
@inproceedings{bd50d29e-e03b-44bc-90f8-eac4b7cb4c13,
  abstract     = {{In this work, we propose a novel measure of distance for quantifying dissimilarities between signals observed on a graph. Building on a recently introduced optimal mass transport framework, the distance measure is formed using the second-order statistics of the graph signals, allowing for comparison of graph processes without direct access to the signals themselves, while explicitly taking the dynamics of the underlying graph into account. The behavior of the proposed distance notion is illustrated in a graph signal classification scenario, indicating attractive modeling properties as compared to the standard Euclidean metric.}},
  author       = {{Juhlin, Maria and Elvander, Filip and Jakobsson, Andreas}},
  booktitle    = {{2019 27th European Signal Processing Conference, EUSIPCO 2019}},
  isbn         = {{978-1-5386-7300-3}},
  language     = {{eng}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{Defining Graph Signal Distances Using an Optimal Mass Transport Framework}},
  url          = {{http://dx.doi.org/10.23919/EUSIPCO.2019.8902502}},
  doi          = {{10.23919/EUSIPCO.2019.8902502}},
  volume       = {{2019}},
  year         = {{2019}},
}