Estimation of Time-Varying Graph Signals by Learning Graph Dictionaries Zamanda Deǧişen Graf Sinyallerinin Kestirimi için Graflarda Sözlük Öǧrenme

Date

2022-01-01

Author

Acar, Abdullah Burak
Vural, Elif

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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We study the problem of estimating time-varying graph signals from missing observations. We propose a method based on learning graph dictionaries specified by a set of time-vertex kernels in the joint spectral domain. The parameters of the time-vertex kernels are optimized jointly with the sparse representation coefficients of the signals, so that the learnt representation fits well to the available observations of the time-vertex signals at hand. The missing observations of the signals are then estimated based on their reconstruction with the learnt model. Experimental results on real graph signal data sets show that the proposed method outperforms classical graph-based regression approaches.

Subject Keywords

graph dictionary learning, graph kernels, Graph signal processing, time-vertex spectrum

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85138737048&origin=inward
https://hdl.handle.net/11511/101501

DOI

https://doi.org/10.1109/siu55565.2022.9864704

Conference Name

30th Signal Processing and Communications Applications Conference, SIU 2022

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

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Citation Formats

A. B. Acar and E. Vural, “Estimation of Time-Varying Graph Signals by Learning Graph Dictionaries Zamanda Deǧişen Graf Sinyallerinin Kestirimi için Graflarda Sözlük Öǧrenme,” presented at the 30th Signal Processing and Communications Applications Conference, SIU 2022, Safranbolu, Türkiye, 2022, Accessed: 00, 2023. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85138737048&origin=inward.