Tuning parameter selection for L_1 type regularization(Session 2b)

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概要

• 論文の詳細を見る

• In sparse regression modeling via regularization such as the lasso, elastic net and bridge regression, it is important to select appropriate values of tuning parameters including regularization parameters. The choice of tuning parameters can be viewed as a model selection and evaluation problem. Mallows' C_p type criterion may be used to choose the tuning parameters, for which the concept of degrees of freedom plays a key role. In the present paper, we propose an efficient algorithm which computes the degrees of freedom sequentially by extending the generalized path seeking algorithm. Monte Carlo simulations demonstrate that our methodology performs well in various situations.

• 2011-11-11

著者

• Konishi Sadanori

  Faculty Of Mathematics Kyushu University

• Hirose Kei

  Division Of Mathematical Science Graduate School Of Engineering Science Osaka University

• Tateishi Shohei

  Toyama Chemical Co. Ltd.

関連論文

• VARIABLE SELECTION IN LOGISTIC DISCRIMINATION BASED ON LOCAL LIKELIHOOD
• LOGISTIC DISCRIMINATION BASED ON REGULARIZED LOCAL LIKELIHOOD METHOD
• Sparse modifying algorithm in Bayesian lasso(Session 4a)
• Tuning parameter selection for L_1 type regularization(Session 2b)

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