Local stationarity of graph signals: insights and experiments

Benjamin Girault; Shrikanth S. Narayanan; Antonio Ortega

doi:10.1117/12.2274584

24 August 2017 Local stationarity of graph signals: insights and experiments

Benjamin Girault, Shrikanth S. Narayanan, Antonio Ortega

Proceedings Volume 10394, Wavelets and Sparsity XVII; 103941P (2017) https://doi.org/10.1117/12.2274584
Event: SPIE Optical Engineering + Applications, 2017, San Diego, California, United States

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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Citation Download Citation

Benjamin Girault, Shrikanth S. Narayanan, and Antonio Ortega "Local stationarity of graph signals: insights and experiments", Proc. SPIE 10394, Wavelets and Sparsity XVII, 103941P (24 August 2017); https://doi.org/10.1117/12.2274584

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