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Vous êtes ici : Accueil / Séminaires / Machine Learning and Signal Processing / Leaving the Euclidean Norm in GSP: Theory, Applications and Perspectives

Leaving the Euclidean Norm in GSP: Theory, Applications and Perspectives

Benjamin Girault (enseignant-chercheur ENSAI, Rennes)
Quand ? Le 06/04/2021,
de 15:00 à 16:00
Participants Benjamin Girault
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Title : Leaving the Euclidean Norm in GSP: Theory, Applications and Perspectives

Asbtract : The literature on graph processing almost always considers the space of graph signals to be
equipped with the standard dot product as inner product. However, several concrete examples
show that this inner product may not be the right choice. In this presentation, we show that
a sound framework for GSP can be built using two key ingredients that can be freely chosen
given an application: the graph signal inner product, and the graph signal variation operator.
We then give several example applications of this framework, either by reinterpreting existing
methods from GSP and image processing, or new methods leveraging the added freedom
allowed by the inner product.

Contact : https://www.benjamin-girault.com

Séminaire en ligne : https://lpensl.my.webex.com/ 

Room : Webconf3