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Proceedings Paper

Local stationarity of graph signals: insights and experiments
Author(s): Benjamin Girault; Shrikanth S. Narayanan; Antonio Ortega
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Paper 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.

Paper Details

Date Published: 24 August 2017
PDF: 17 pages
Proc. SPIE 10394, Wavelets and Sparsity XVII, 103941P (24 August 2017); doi: 10.1117/12.2274584
Show Author Affiliations
Benjamin Girault, The Univ. of Southern California (United States)
Shrikanth S. Narayanan, The Univ. of Southern California (United States)
Antonio Ortega, The Univ. of Southern California (United States)


Published in SPIE Proceedings Vol. 10394:
Wavelets and Sparsity XVII
Yue M. Lu; Dimitri Van De Ville; Manos Papadakis, Editor(s)

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