Comptes Rendus
no. 5
Fourier and the science of today / Fourier et la science d'aujourd'hui
Fourier could be a data scientist: From graph Fourier transform to signal processing on graphs
[Fourier serait un data scientist : de la transformée de Fourier sur graphe au traitement du signal sur graphe]
Benjamin Ricaud1 ; Pierre Borgnat2  ; Nicolas Tremblay3 ; Paulo Gonçalves4 ; Pierre Vandergheynst1
1 Signal Processing Laboratory 2 (LTS2), École polytechnique fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
2 Université de Lyon, CNRS, ENS de Lyon, UCB Lyon-1, Laboratoire de physique, UMR 5672, 69342 Lyon, France
3 CNRS, Université Grenoble-Alpes, Gipsa-lab, France
4 Université de Lyon, Inria, CNRS, ENS de Lyon, UCB Lyon-1, LIP UMR 5668, 69342 Lyon, France
Comptes Rendus. Physique, Fourier and the science of today / Fourier et la science d’aujourd’hui, Volume 20 (2019) no. 5, pp. 474-488.

Les travaux de Joseph Fourier se sont avérés extrêmement féconds, et la transformée portant son nom est, aujourd'hui encore, incontournable. La souplesse de cette analyse, son efficacité calculatoire et l'interprétation physique qu'elle offre la met au cœur de nombreux domaines scientifiques. Avec l'explosion du nombre et de la diversité des données numériques, la généralisation des outils d'analyse s'appuyant sur la transformation de Fourier est plus que jamais nécessaire. C'est en particulier le cas en science des données, et spécifiquement en science des réseaux. De nouveaux problèmes se posent quant à l'extraction d'information à partir de données qui ont des structures irrégulières, comme des réseaux sociaux, biologiques ou autres données sur des graphes potentiellement arbitraires. Le traitement du signal sur graphe est une des directions prometteuses dédiées à ce type de données. Ce texte présente un état de l'art du domaine, en se concentrant d'abord sur la façon de définir une transformée de Fourier pour des données sur graphes, comment l'interpréter et enfin comment l'utiliser pour étudier ces données. Il se termine par une discussion sur de possibles utilisations. Ce faisant, ce travail illustre en quoi la démarche de Fourier reste moderne et universelle et montre comment ses idées, essentiellement issues de la physique, puis enrichies par les mathématiques, l'informatique et la théorie du signal, demeurent essentielles pour répondre aux défis actuels en science des données.

The legacy of Joseph Fourier in science is vast, especially thanks to the essential tool that the Fourier transform is. The flexibility of this analysis, its computational efficiency and the physical interpretation it offers makes it a cornerstone in many scientific domains. With the explosion of digital data, both in quantity and diversity, the generalization of the tools based on Fourier transform is mandatory. In data science, new problems arose for the processing of irregular data such as social networks, biological networks or other data on networks. Graph signal processing is a promising approach to deal with those. The present text is an overview of the state of the art in graph signal processing, focusing on how to define a Fourier transform for data on graphs, how to interpret it and how to use it to process such data. It closes showing some examples of use. Along the way, the review reveals how Fourier's work remains modern and universal, and how his concepts, coming from physics and blended with mathematics, computer science, and signal processing, play a key role in answering the modern challenges in data science.

Publié le :
DOI : 10.1016/j.crhy.2019.08.003
Keywords: Graph signal processing, Fourier transform, Wavelets, Data science, Machine learning
Mots-clés : Traitement du signal sur graphe, Transformée de Fourier, Ondelettes, Science des données, Apprentissage machine

Benjamin Ricaud 1 ; Pierre Borgnat 2 ; Nicolas Tremblay 3 ; Paulo Gonçalves 4 ; Pierre Vandergheynst 1

1 Signal Processing Laboratory 2 (LTS2), École polytechnique fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
2 Université de Lyon, CNRS, ENS de Lyon, UCB Lyon-1, Laboratoire de physique, UMR 5672, 69342 Lyon, France
3 CNRS, Université Grenoble-Alpes, Gipsa-lab, France
4 Université de Lyon, Inria, CNRS, ENS de Lyon, UCB Lyon-1, LIP UMR 5668, 69342 Lyon, France 
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Benjamin Ricaud; Pierre Borgnat; Nicolas Tremblay; Paulo Gonçalves; Pierre Vandergheynst. Fourier could be a data scientist: From graph Fourier transform to signal processing on graphs. Comptes Rendus. Physique, Fourier and the science of today / Fourier et la science d’aujourd’hui, Volume 20 (2019) no. 5, pp. 474-488. doi : 10.1016/j.crhy.2019.08.003. https://comptes-rendus.academie-sciences.fr/physique/articles/10.1016/j.crhy.2019.08.003/

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