Efficient regularization parameter selection via information criteria
Efficient regularization parameter selection via information criteria
复制标题
通过信息标准进行有效的正则化参数选择
DOI：
10.1080/03610918.2011.639969
复制
发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Araki,Y.
中科院分区：
文献类型：
--
作者：
高橋邦彦;武村真治;他;Araki,Y.
In a nonlinear regression model based on a regularization method, selection of appropriate regularization parameters is crucial. Information criteria such as generalized information criterion (GIC) and generalized Bayesian information criterion (GBIC) are useful for selecting the optimal regularization parameters. However, the optimal parameter is often determined by calculating information criterion for all candidate regularization parameters, and so the computational cost is high. One simple method by which to accomplish this is to regard GIC or GBIC as a function of the regularization parameters and to find a value minimizing GIC or GBIC. However, it is unclear how to solve the optimization problem. In the present article, we propose an efficient Newton–Raphson type iterative method for selecting optimal regularization parameters with respect to GIC or GBIC in a nonlinear regression model based on basis expansions. This method reduces the computational time remarkably compared to the grid search and can select more suitable regularization parameters. The effectiveness of the method is illustrated through real data examples.
