The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
올가미 회귀 기반 L1 정규화는 가장 널리 사용되는 희소 추정 방법 중 하나입니다. 정규화 정도를 결정하는 정규화 매개변수를 미리 적절하게 설정해 주어야 하는 경우가 많습니다. 경험적 베이즈 접근법은 정규화 매개변수를 추정하는 효과적인 방법을 제공하지만 해당 솔루션은 아직 올가미 회귀 모델에서 완전히 조사되지 않았습니다. 본 연구에서는 올가미 회귀의 단일 매개변수 모델의 경험적 베이즈 추정기를 분석하고 그 고유성과 특성을 보여줍니다. 또한, 이 추정기를 변분 근사치와 비교하여 정확도를 평가합니다.
Tsukasa YOSHIDA
Toyohashi University of Technology
Kazuho WATANABE
Toyohashi University of Technology
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Tsukasa YOSHIDA, Kazuho WATANABE, "Empirical Bayes Estimation for L1 Regularization: A Detailed Analysis in the One-Parameter Lasso Model" in IEICE TRANSACTIONS on Fundamentals,
vol. E101-A, no. 12, pp. 2184-2191, December 2018, doi: 10.1587/transfun.E101.A.2184.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E101.A.2184/_p
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@ARTICLE{e101-a_12_2184,
author={Tsukasa YOSHIDA, Kazuho WATANABE, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Empirical Bayes Estimation for L1 Regularization: A Detailed Analysis in the One-Parameter Lasso Model},
year={2018},
volume={E101-A},
number={12},
pages={2184-2191},
abstract={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.},
keywords={},
doi={10.1587/transfun.E101.A.2184},
ISSN={1745-1337},
month={December},}
부
TY - JOUR
TI - Empirical Bayes Estimation for L1 Regularization: A Detailed Analysis in the One-Parameter Lasso Model
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2184
EP - 2191
AU - Tsukasa YOSHIDA
AU - Kazuho WATANABE
PY - 2018
DO - 10.1587/transfun.E101.A.2184
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E101-A
IS - 12
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - December 2018
AB - 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.
ER -