Local stationarity of graph signals: insights and experiments

Abstract : In this paper, we look at one of the most crucial ingredient to graph signal processing: the graph. By taking a step back on the conventional approach using Gaussian weights, we are able to obtain a better spectral representation of a stochastic graph signal. Our approach focuses on learning the weights of the graphs, thus enabling better richness in the structure by incorporating both the distance and the local structure into the weights. Our results show that the graph power spectrum we obtain is closer to what we expect, and stationarity is better preserved when going from a continuous signal to its sampled counterpart on the graph. We further validate the approach on a real weather dataset.
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Communication dans un congrès
Wavelets and Sparsity XVII, Aug 2017, San Diego, United States. Proceedings SPIE 10394, Wavelets and Sparsity XVII, 〈https://spie.org/conferences-and-exhibitions/past-conferences-and-exhibitions/optics-and-photonics-2017〉. 〈10.1117/12.2274584〉
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https://hal.inria.fr/hal-01574215
Contributeur : Benjamin Girault <>
Soumis le : vendredi 11 août 2017 - 21:59:12
Dernière modification le : vendredi 27 avril 2018 - 19:54:00

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Benjamin Girault, Shrikanth Narayanan, Antonio Ortega. Local stationarity of graph signals: insights and experiments. Wavelets and Sparsity XVII, Aug 2017, San Diego, United States. Proceedings SPIE 10394, Wavelets and Sparsity XVII, 〈https://spie.org/conferences-and-exhibitions/past-conferences-and-exhibitions/optics-and-photonics-2017〉. 〈10.1117/12.2274584〉. 〈hal-01574215〉

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