Towards Stationary Time-Vertex Signal Processing

Graph-based methods for signal processing have shown promise for the analysis of data exhibiting irregular structure, such as those found in social, transportation, and sensor networks. Yet, though these systems are often dynamic, state-of-the-art methods for graph signal processing ignore the time dimension. To address this shortcoming, this paper considers the statistical analysis of time-varying graph signals. We introduce a novel definition of joint (time-vertex) stationarity, which generalizes the classical definition of time stationarity and the recent definition appropriate for graphs. This gives rise to a scalable Wiener optimization framework for denoising, semi-supervised learning, or more generally inverting a linear operator, that is provably optimal. Experimental results on real weather data demonstrate that taking into account graph and time dimensions jointly can yield significant accuracy improvements in the reconstruction effort.


Published in:
2017 Ieee International Conference On Acoustics, Speech And Signal Processing (Icassp), 3914-3918
Presented at:
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), New Orleans, LA, MAR 05-09, 2017
Year:
2017
Publisher:
New York, Ieee
ISSN:
1520-6149
ISBN:
978-1-5090-4117-6
Keywords:
Laboratories:




 Record created 2018-01-15, last modified 2018-09-13


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