Graphs are pervasive in various applications capturing the complex behavior observed in biological, financial, and social networks, to name a few. Two major learning tasks over graphs are topology identification and inference of signals evolving over graphs. Existing approaches typically aim at identifying the topology when signals on all nodes are observed, or, recovering graph signals over networks with known topologies. In practice however, signal or graph perturbations can be present in both tasks, due to model mismatch, outliers, outages or adversaries. To cope with these perturbations, this work introduces regularized total least-squares (TLS) based approaches and corresponding alternating minimization algorithms with convergence guarantees. Tests on simulated data corroborate the effectiveness of the novel TLS-based approaches.
|Original language||English (US)|
|Title of host publication||Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018|
|Editors||Michael B. Matthews|
|Publisher||IEEE Computer Society|
|Number of pages||5|
|State||Published - Feb 19 2019|
|Event||52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018 - Pacific Grove, United States|
Duration: Oct 28 2018 → Oct 31 2018
|Name||Conference Record - Asilomar Conference on Signals, Systems and Computers|
|Conference||52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018|
|Period||10/28/18 → 10/31/18|
Bibliographical noteFunding Information:
Work in this paper was supported by grants NSF 1711471, 1500713 and NIH 1R01GM104975-01.
© 2018 IEEE.
- Graph and signal perturbations
- graph signal reconstruction
- structural equation models
- topology identification
- total leastsquares