Online Time-Varying Topology Identification Via Prediction-Correction Algorithms
Type
Conference PaperKAUST Grant Number
OSR-2015-Sensors-2700Date
2021-06-06Preprint Posting Date
2020-10-22Permanent link to this record
http://hdl.handle.net/10754/665802
Metadata
Show full item recordAbstract
Signal processing and machine learning algorithms for data supported over graphs, require the knowledge of the graph topology. Unless this information is given by the physics of the problem (e.g., water supply networks, power grids), the topology has to be learned from data. Topology identification is a challenging task, as the problem is often ill-posed, and becomes even harder when the graph structure is time-varying. In this paper, we address the problem of dynamic topology identification by building on recent results from time-varying optimization, devising a general-purpose online algorithm operating in non-stationary environments. Because of its iteration-constrained nature, the proposed approach exhibits an intrinsic temporal-regularization of the graph topology without explicitly enforcing it. As a case-study, we specialize our method to the Gaussian graphical model (GGM) problem and corroborate its performance.Citation
Natali, A., Coutino, M., Isufi, E., & Leus, G. (2021). Online Time-Varying Topology Identification Via Prediction-Correction Algorithms. ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). doi:10.1109/icassp39728.2021.9415053Sponsors
This work was supported in parts by the KAUST-MIT-TUD consortium grant OSR-2015-Sensors-2700. Mario Coutino is partially supported by CONACYT.Publisher
IEEEConference/Event name
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)ISBN
9781728176055arXiv
2010.11634Additional Links
https://ieeexplore.ieee.org/document/9415053/ae974a485f413a2113503eed53cd6c53
10.1109/icassp39728.2021.9415053