Linear structural vector autoregressive models constitute a generalization of structural equation models (SEMs) and vector autoregressive (VAR) models, two popular approaches for topology inference of directed graphs. Although simple and tractable, linear SVARMs seldom capture nonlinearities that are inherent to complex systems, such as the human brain. To this end, the present paper advocates kernel-based nonlinear SVARMs, and develops an efficient sparsity-promoting least-squares estimator to learn the hidden topology. Numerical tests on real electrocorticographic (ECoG) data from an Epilepsy study corroborate the efficacy of the novel approach.
|Original language||English (US)|
|Title of host publication||2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|State||Published - Jun 16 2017|
|Event||2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, United States|
Duration: Mar 5 2017 → Mar 9 2017
|Name||ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings|
|Other||2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017|
|Period||3/5/17 → 3/9/17|
Bibliographical noteFunding Information:
Work in this paper was supported by grants NSF 1500713, 1514056 and NIH 1R01GM104975-01.
© 2017 IEEE.
Copyright 2017 Elsevier B.V., All rights reserved.
- Network topology inference
- structural vector autoregressive models