Linear structural equation models (SEMs) have been very successful in identifying the topology of complex graphs, such as those representing social and brain networks. In many cases however, the presence of highly correlated nodes hinders performance of the available SEM estimators that rely on the least-Absolute shrinkage and selection operator (LASSO). To this end, an elastic net based SEM is put forth, to infer causal relations between nodes belonging to networks, in the presence of highly correlated data. An efficient algorithm based on the alternating direction method of multipliers (ADMM) is developed, and preliminary tests on synthetic as well as real data demonstrate the effectiveness of the proposed approach.
|Original language||English (US)|
|Title of host publication||25th European Signal Processing Conference, EUSIPCO 2017|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|State||Published - Oct 23 2017|
|Event||25th European Signal Processing Conference, EUSIPCO 2017 - Kos, Greece|
Duration: Aug 28 2017 → Sep 2 2017
|Name||25th European Signal Processing Conference, EUSIPCO 2017|
|Other||25th European Signal Processing Conference, EUSIPCO 2017|
|Period||8/28/17 → 9/2/17|
Bibliographical notePublisher Copyright:
© EURASIP 2017.
- Elastic Net
- Structural Equation Models
- Topology inference