Learning Time-Vertex Dictionaries for Estimating Time-Varying Graph Signals

Date

2022-01-01

Author

Acar, Abdullah Burak
Vural, Elif

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In this work, we study the problem of learning time-vertex dictionaries for the modeling and estimation of time-varying graph signals. We consider a setting with a collection of partially observed time-varying graph signals, and propose a solution for the estimation of the missing signal observations by learning time-vertex dictionaries from the available observations. We adopt a time-vertex dictionary model defined through a set of joint time-vertex spectral kernels, each of which captures a different spectral component of the signals in their joint time-vertex spectrum. The kernel parameters are optimized along with the representations of the signals so as to be coherent with the available signal observations. Experimental results show that the proposed method yields promising estimation performance in comparison with non-adaptive graph dictionary models and baseline classical graph regression methods.

Subject Keywords

graph dictionary learning, Graph signal processing, time-vertex joint spectrum, time-vertex representation learning, time-vertex signals

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85142687274&origin=inward
https://hdl.handle.net/11511/101625

DOI

https://doi.org/10.1109/mlsp55214.2022.9943416

Conference Name

32nd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2022

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

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Citation Formats

A. B. Acar and E. Vural, “Learning Time-Vertex Dictionaries for Estimating Time-Varying Graph Signals,” Xian, Çin, 2022, vol. 2022-August, Accessed: 00, 2023. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85142687274&origin=inward.