Regularization Parameter Selection in the Group Lasso
نویسندگان
چکیده
This article discusses the problem of choosing a regularization parameter in the group Lasso proposed by Yuan and Lin (2006), an l1-regularization approach for producing a block-wise sparse model that has been attracted a lot of interests in statistics, machine learning, and data mining. It is important to choose an appropriate regularization parameter from a set of candidate values, because it affects the predictive performance of the fitted model. However, traditional model selection criteria, such as AIC and BIC, cover only models estimated by maximum likelihood estimation and can not be directly applied for regularization parameter selection. We propose an information criterion for regularization parameter selection in the group Lasso in the framework of maximum penalized likelihood estimation.
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