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

Show full item record

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

Item Usage Stats

148
views

0
downloads

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

Suggestions

OpenMETU
Core

Learning Graph Signal Representations with Narrowband Spectral Kernels Kar, Osman Furkan; Turhan, Gülce; Vural, Elif (2022-01-01) In this work, we study the problem of learning graph dictionary models from partially observed graph signals. We represent graph signals in terms of atoms generated by narrowband graph kernels. We formulate an optimization problem where the kernel parameters are learnt jointly with the signal representations under a triple regularization scheme: While the first regularization term aims to control the spectrum of the narrowband kernels, the second term encourages the reconstructed graph signals to vary smoot...
Learning Time-Vertex Dictionaries for Estimating Time-Varying Graph Signals Acar, Abdullah Burak; Vural, Elif (2022-01-01) 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 spec...
Learning Parametric Time-Vertex Graph Processes from Incomplete Realizations Guneyi, Eylem Tugce; Canbolat, Abdullah; Vural, Elif (2021-01-01) © 2021 IEEE.We consider the problem of estimating time-varying graph signals with missing observations, which is of interest in many applications involving data acquisition on irregular topologies. We model time-varying graph signals as jointly stationary time-vertex ARMA graph processes. We formulate the learning of ARMA process parameters as an optimization problem where the joint power spectral density of the model is fit to a rough empirical estimate of the process covariance matrix. We propose a convex...
Investigation of Stationarity for Graph Time Series Data Sets Güneyi, Eylem Tuğçe; Vural, Elif (2021-01-07) Graphs permit the analysis of the relationships in complex data sets effectively. Stationarity is a feature that facilitates the analysis and processing of random time signals. Since graphs have an irregular structure, the definition of classical stationarity does not apply to graphs. In this study, we study how stationarity is defined for graph random processes and examine the validity of the stationarity assumption with experiments on synthetic and real data sets.
Learning Narrowband Graph Spectral Kernels for Graph Signal Estimation Çizge Sinyallerinin Dar Bantli Spektral Kernel Öǧrenimi ile Kestirimi Furkan Kar, Osman; Turhan, Gülce; Vural, Elif (2022-01-01) 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 gr...

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.