Empirical Bayes Estimation for L1 Regularization: A Detailed Analysis in the One-Parameter Lasso Model
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- YOSHIDA Tsukasa
- Dept. of Computer Science and Engineering, Toyohashi University of Technology
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- WATANABE Kazuho
- Dept. of Computer Science and Engineering, Toyohashi University of Technology
書誌事項
- タイトル別名
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- Empirical Bayes Estimation for <i>L</i><sub>1</sub> Regularization: A Detailed Analysis in the One-Parameter Lasso Model
説明
<p>Lasso regression based on the L1 regularization is one of the most popular sparse estimation methods. It is often required to set appropriately in advance the regularization parameter that determines the degree of regularization. Although the empirical Bayes approach provides an effective method to estimate the regularization parameter, its solution has yet to be fully investigated in the lasso regression model. In this study, we analyze the empirical Bayes estimator of the one-parameter model of lasso regression and show its uniqueness and its properties. Furthermore, we compare this estimator with that of the variational approximation, and its accuracy is evaluated.</p>
収録刊行物
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- IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
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IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E101.A (12), 2184-2191, 2018-12-01
一般社団法人 電子情報通信学会
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詳細情報 詳細情報について
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- CRID
- 1390282763072368640
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- NII論文ID
- 130007539073
- 120006576784
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- ISSN
- 17451337
- 09168508
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- IRDB
- Crossref
- CiNii Articles
- KAKEN
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- 抄録ライセンスフラグ
- 使用不可