Learning Narrowband Graph Spectral Kernels for Graph Signal Estimation Çizge Sinyallerinin Dar Bantli Spektral Kernel Öǧrenimi ile Kestirimi

Date

2022-01-01

Author

Furkan Kar, Osman
Turhan, Gülce
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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In this work, we study the problem of estimating graph signals from incomplete observations. We propose a method that learns the spectrum of the graph signal collection at hand by fitting a set of narrowband graph kernels to the observed signal values. The unobserved graph signal values are then estimated using the sparse representations of the signals in the graph dictionary formed by the learnt kernels. Experimental results on graph data sets show that the proposed method compares favorably to baseline graph-based semi-supervised regression solutions.

Subject Keywords

graph dictionary learning, graph kernels, Graph signal processing, narrow-band kernels

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85138673672&origin=inward
https://hdl.handle.net/11511/101540

DOI

https://doi.org/10.1109/siu55565.2022.9864966

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

O. Furkan Kar, G. Turhan, and E. Vural, “Learning Narrowband Graph Spectral Kernels for Graph Signal Estimation Çizge Sinyallerinin Dar Bantli Spektral Kernel Öǧrenimi ile Kestirimi,” 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=85138673672&origin=inward.