Volume 37, Number 6, December 2019
|Page(s)||1320 - 1325|
|Published online||11 February 2020|
Learning the Structure of Hub Network Based on Graph Model
School of Mathematics, Northwest University, Xi'an 710127, China
2 Xi'an Satellite Control Centre, Xi'an 710043, China
3 State Key Laboratory of Astronautic Dynamics, Xi'an 710043, China
In this paper, we focus on the structure learning problem of the hub network. In the neighborhood selection framework, we use the L1 and L2 regularizers to incorporate the sparse and group prior of the hub network, so as to make the network easier to generate Hub. We employ the coordinate descent algorithm to solve the resulting model. Simulation and real data analysis show that the proposed method is effective and applicable in parameter estimation and model selection, and results illustrate the influence ability of the control parameter on the model.
Key words: graphical model / network / Hub / neighborhood selection
关键字 : 图模型 / 网络 / Hub / 邻域选择
© 2019 Journal of Northwestern Polytechnical University. All rights reserved.
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