Learning Parametric Time-Vertex Graph Processes from Incomplete Realizations

Date

2021-01-01

Author

Guneyi, Eylem Tugce
Canbolat, Abdullah
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

124
views

0
downloads

© 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 relaxation of this problem, which results in an algorithm more flexible than existing methods regarding the pattern of available and missing observations of the process. Experimental results on meteorological signals show that the proposed method compares favorably to reference state-of-the-art algorithms.

Subject Keywords

Graph processes, stationarity, time-vertex joint processes, ARMA models, SERIES, ARMA models, Graph processes, stationarity, time-vertex joint processes

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85122797289&origin=inward
https://hdl.handle.net/11511/99188

DOI

https://doi.org/10.1109/mlsp52302.2021.9596563

Conference Name

31st IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2021

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

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...
Estimation of Time-Varying Graph Signals by Learning Graph Dictionaries Zamanda Deǧişen Graf Sinyallerinin Kestirimi için Graflarda Sözlük Öǧrenme Acar, Abdullah Burak; Vural, Elif (2022-01-01) 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 b...
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.
Estimation of Locally Stationary Graph Processes from Incomplete Realizations Canbolat, Abdullah; Vural, Elif (2022-01-01) Stationarity is a well-studied concept in signal processing and the concept of stationary random processes has been extended to graph domains in several recent works. Meanwhile, in many scenarios a globally stationary process model may fail to accurately represent the correlation patterns of the data on the whole graph, e.g. when data is acquired on big graphs or when the behavior of the process varies significantly throughout the graph. In this work, we first propose a locally stationary graph process mode...
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...

Citation Formats

E. T. Guneyi, A. Canbolat, and E. Vural, “Learning Parametric Time-Vertex Graph Processes from Incomplete Realizations,” Gold-Coast, Avustralya, 2021, vol. 2021-October, Accessed: 00, 2022. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85122797289&origin=inward.