In more formal terms, a graph signal is a function defined on the nodes of a graph and can be represented as a vector with one component per graph node. In order to enjoy the promised benefits of GSP methods, the knowledge of the underlying graph is needed, which is a strong requirement for many real-world problems where the graph may be little or not at all known.
In this work we are going to study the data-driven Graph Learning problem where the objective is to use Machine Learning techniques to infer the underlying graph based on observed graph signals. In nature, this is an ill-posed problem since many graphs may be able to explain equally well the data. Therefore, the challenge is to introduce the right assumptions regarding the graph signals, the graph, and the interrelation between the two in order to solve tractable optimization problems to reach meaningful solutions. A survey of existing works is part of the mission of the project, as for target applications, those include physiological data such as fMRI, epidemiological data, or complex data from several other sources.
graph signal processing, graph theory, sparse coding, graph inference
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 Dong, X., Thanou, D., and Frossard, P. (2018). « Learning Graphs from Data: A Signal Representation Perspective », arxiv preprint.
 Le Bars, B., Humbert, P., Oudre, L., amd Kalogeratos, A. (2019). “Learning Laplacian Matrix from Bandlimited Graph Signals”, IEEE International Conference on Acoustics, Speech, and Signal Processing.
Poste d'agrégé-préparateur au DER de Mathématiques de l'ENS Paris-Saclay pour la rentrée universitaire de 2020 - REF GALAXIE 4140
La marche humaine est un phénomène complexe mais également structuré. Chez le sujet sain,elles est caractérisée par une certaine régularité et symétrie.