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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
Metadata
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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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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.