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论文:2019,Vol:37,Issue(6):1320-1325 |
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引用本文： |
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张重阳, 郭骁, 张海. 基于图模型的Hub网络的结构学习[J]. 西北工业大学学报 |
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ZHANG Chongyang, GUO Xiao, ZHANG Hai. Learning the Structure of Hub Network Based on Graph Model[J]. Northwestern polytechnical university |
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| 基于图模型的Hub网络的结构学习 |
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| 张重阳1,2,3, 郭骁1, 张海1 |
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1. 西北大学 数学学院, 陕西 西安 710127;
2. 中国西安卫星测控中心, 陕西 西安 710043;
3. 宇航动力学国家重点实验室, 陕西 西安 710043 |
| 摘要: |
| 聚焦于具有Hub的网络结构学习问题。在邻域选择框架下，基于Hub网络的特点在模型中加入L1和L2正则子，从而分别引入网络的稀疏性先验和Hub网络的组先验，使所得网络更容易产生Hub。对于所得模型，采用坐标下降法求解。模拟数据和实际数据实验表明所提模型在参数估计、模型选择方面的有效性和实用性，并说明了调控参数对模型的影响。 |
| 关键词:
图模型
网络
Hub
邻域选择
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| Learning the Structure of Hub Network Based on Graph Model |
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| ZHANG Chongyang1,2,3, GUO Xiao1, ZHANG Hai1 |
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1. 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 |
| Abstract: |
| 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
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| 收稿日期: 2018-10-28
修回日期:
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| DOI: 10.1051/jnwpu/20193761320 |
| 基金项目: 国家自然科学基金（11571011）资助 |
| 通讯作者:
Email： |
| 作者简介: 张重阳(1994-),女,西北大学硕士研究生,主要从事复杂网络及图模型研究。
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| 相关功能 |
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| 作者相关文章 |
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| 张重阳 在本刊中的所有文章 |
| 郭骁 在本刊中的所有文章 |
| 张海 在本刊中的所有文章 |
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参考文献: |
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