正则化模型在复杂网络结构中的研究
Research on Regularization Model in Complex Network Structure
DOI: 10.12677/AAM.2019.89178, PDF,   
作者: 宗瑞雪, 钟小敏：广西大学，数学与信息科学学院，广西 南宁
关键词: 复杂网络；正则化模型；Maxdet算法；模型选择；Complex Network； Regularization Model； Maxdet Algorithm； Model Selection
摘要: 研究基于高斯图模型下的复杂网络结构，考虑惩罚项为The seamless-L0惩罚，提出了新的正则化模型。利用Minorize-Maximization算法，构造所提正则化模型的Minorization函数，再对该函数利用Maxdet算法求解模型。该模型同时进行模型选择和参数估计，并且估计结果具有稀疏性。数值模拟研究和基因调控网络的实例说明该模型有良好的参数估计和模型选择效果。
Abstract: Based on the complex network structure under the Guassian model, the penalty term is considered as the seamless-L0 penalty, and a new regularization model is proposed. The Minorize-Maximization algorithm was used to construct the Minorization function of the proposed regularization model, and then the Maxdet algorithm was used to solve the model. The model simultaneously performs model selection and parameter estimation. And the result is sparse. Numerical simulation studies and examples of gene regulatory networks show that the model has good parameter estimation and model selection effects.
文章引用：宗瑞雪, 钟小敏. 正则化模型在复杂网络结构中的研究[J]. 应用数学进展, 2019, 8(9): 1522-1529. https://doi.org/10.12677/AAM.2019.89178

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