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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
Metadata
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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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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.