Learning Parametric Time-Vertex Graph Processes from Incomplete Realizations

Guneyi, Eylem Tugce
Canbolat, Abdullah
Vural, Elif
© 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.
31st IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2021


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.
Measurement of chi(c1) and chi(c2) production with root s=7 TeV pp collisions at ATLAS
Aad, G.; et. al. (2014-07-01)
The prompt and non-prompt production cross-sections for the chi(c1) and chi(c2) charmonium states are measured in pp collisions at root s = 7TeV with the ATLAS detector at the LHC using 4.5 fb(-1) of integrated luminosity. The chi(c) states are reconstructed through the radiative decay chi c -> J/psi gamma ( with J/psi -> mu(+)mu(-)) where photons are reconstructed from gamma -> e(+)e(-) conversions. The production rate of the chi(c2) state relative to the chi(c1) state is measured for prompt and non-prompt...
Güneyi, Eylem Tuğçe; Vural, Elif; Department of Electrical and Electronics Engineering (2021-9-8)
Graph models provide efficient tools for analyzing data defined over irregular domains such as social networks, sensor networks, and transportation networks. Real-world graph signals are usually time-varying signals. The characterization of the joint behavior of time-varying graph signals in the time and the vertex domains has recently arisen as an interesting research problem, contrasted to the independent processing of graph signals acquired at different time instants. The concept of wide sense stationari...
Learning Parameters of ptSTL Formulas with Backpropagation
Ketenci, Ahmet; Aydın Göl, Ebru (2020-01-01)
In this paper, a backpropagation based algorithm is presented to learn parameters of past time Signal Temporal Logic (ptSTL) formulas. A differentiable weight matrix over the parameter values and a loss function based on the mismatch value of the corresponding formulas over the labeled dataset are used in the algorithm. Analysis over a sample dataset shows that the algorithm solves the ptSTL parameter synthesis problem in an efficient way.
Multi-target tracking using passive doppler measurements
Guldogan, Mehmet B.; Orguner, Umut; Gustafsson, Fredrik (2013-04-26)
In this paper, we analyze the performance of the Gaussian mixture probability hypothesis density (GM-PHD) filter in tracking multiple non-cooperative targets using Doppler-only measurements in a passive sensor network. Clutter, missed detections and multi-static Doppler variances are incorporated into a realistic multi-target scenario. Simulation results show that the GM-PHD filter successfully tracks multiple targets using only Doppler shift measurements in a passive multi-static scenario.
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.