Defining Graph Signal Distances Using an Optimal Mass Transport Framework

Forskningsoutput: Kapitel i bok/rapport/Conference proceedingKonferenspaper i proceedingPeer review


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.
Titel på värdpublikation2019 27th European Signal Processing Conference, EUSIPCO 2019
FörlagIEEE - Institute of Electrical and Electronics Engineers Inc.
Antal sidor5
ISBN (elektroniskt)978-9-0827-9703-9
ISBN (tryckt)978-1-5386-7300-3
StatusPublished - 2019
Evenemang27th European Signal Processing Conference (EUSIPCO) - A Coruna, Spanien
Varaktighet: 2019 sep. 22019 sep. 6


Konferens27th European Signal Processing Conference (EUSIPCO)
OrtA Coruna

Ämnesklassifikation (UKÄ)

  • Signalbehandling
  • Sannolikhetsteori och statistik


Utforska forskningsämnen för ”Defining Graph Signal Distances Using an Optimal Mass Transport Framework”. Tillsammans bildar de ett unikt fingeravtryck.

Citera det här