Name
Abstract | ||
|---|---|---|
Network-science related applications frequently deal with inference of spatio-temporal processes. Such inference tasks can be aided by a graph whose topology contributes to the underlying spatio-temporal dependencies. Contemporary approaches extrapolate dynamic processes relying on a fixed dynamical model, that is not adaptive to changes in the dynamics. Alleviating this limitation, the present work adopts a candidate set of graph-adaptive dynamical models with one active at any given time. Given partially observed nodal samples, a scalable Bayesian tracker is leveraged to infer the graph processes and learn the active dynamical model simultaneously in a data-driven fashion. The resulting algorithm is termed graph-adaptive interacting multiple dynamical models (Grad-IMDM). Numerical tests with synthetic and real data corroborate that the proposed Grad-IMDM is capable of tracking the graph processes and adapting to the dynamical model that best fits the data. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/IEEECONF44664.2019.9048993 | CONFERENCE RECORD OF THE 2019 FIFTY-THIRD ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS & COMPUTERS |
Keywords | DocType | ISSN |
Spatiotemporal process, multiple dynamical models, Bayesian tracker | Conference | 1058-6393 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qin Lu | 1 | 0 | 0.34 |
| Vassilis N. Ioannidis | 2 | 14 | 7.34 |
| G. B. Giannakis | 3 | 11464 | 1206.47 |
| Mario Coutino | 4 | 16 | 7.12 |