From Event: SPIE Optical Engineering + Applications, 2017
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.
Benjamin Girault, Shrikanth S. Narayanan, and Antonio Ortega, "Local stationarity of graph signals: insights and experiments," Proc. SPIE 10394, Wavelets and Sparsity XVII, 103941P (Presented at SPIE Optical Engineering + Applications: August 09, 2017; Published: 24 August 2017); https://doi.org/10.1117/12.2274584.
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