Results 11 to 20 of about 5,741 (199)
Tractable Evaluation of Stein's Unbiased Risk Estimate With Convex Regularizers
IEEE Transactions on Signal Processing, 2023 Stein's unbiased risk estimate (SURE) gives an unbiased estimate of the $\ell_2$ risk of any estimator of the mean of a Gaussian random vector. We focus here on the case when the estimator minimizes a quadratic loss term plus a convex regularizer. For these estimators SURE can be evaluated analytically for a few special cases, and generically using ...
Parth Nobel +2 more
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A comment on Stein's unbiased risk estimate for reduced rank estimators [PDF]
, 2017 In the framework of matrix valued observables with low rank means, Stein's unbiased risk estimate (SURE) can be useful for risk estimation and for tuning the amount of shrinkage towards low rank matrices. This was demonstrated by Cand s et al. (2013) for singular value soft thresholding, which is a Lipschitz continuous estimator.
Niels Richard Hansen
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From Stein's Unbiased Risk Estimates to the Method of Generalized Cross Validation [PDF]
The Annals of Statistics, 1985 Let \(y_i = \mu_i + \varepsilon_i\), \(i=1,\ldots,n\), where \(y_1,\ldots,y_n\) are \(n\) independent observations with unknown means \(\mu_1,\ldots,\mu_n\) and \(\varepsilon_i\) has mean 0 and common variance \(\sigma^2\). To estimate \(\underline {\mu}= (\mu_1,\ldots,\mu_n)'\), a class of linear estimators \(\hat{\mu}(h) = M(h)\underline {y}\) is ...
Ker-Chau Li
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A noise reduction approach based on Stein's unbiased risk estimate [PDF]
ScienceAsia, 2012 This paper proposes a new wavelet-based shrinkage function for 1D signal noise reduction. This shrinkage function adopts the intrascale correlations between wavelet coefficients and exploits Stein's unbiased risk estimator to achieve the optimal parameter.
Q. Guo, Cai-ming Zhang, Erhuan Dong
semanticscholar +2 more sources
Wavelet threshold based on Stein's unbiased risk estimators of restricted location parameter in multivariate normal. [PDF]
J Appl Stat, 2021 In this paper, the problem of estimating the mean vector under non-negative constraints on location vector of the multivariate normal distribution is investigated. The value of the wavelet threshold based on Stein's unbiased risk estimators is calculated for the shrinkage estimator in restricted parameter space.
Karamikabir H, Afshari M, Lak F.
europepmc +3 more sources
Image Denoising Based on Nonlocal Bayesian Singular Value Thresholding and Stein’s Unbiased Risk Estimator [PDF]
IEEE Transactions on Image Processing, 2019 Singular value thresholding (SVT)- or nuclear norm minimization (NNM)-based nonlocal image denoising methods often rely on the precise estimation of the noise variance.
Caoyuan Li +6 more
semanticscholar +4 more sources
Journal of Multivariate Analysis, 2020 This paper studies regularized estimators obtained by means of two polyhedral convex classes of regularizators for the regression model defined through submodular functions, the Lovász extension regularization and submodular norm regularization. Characterizations are obtained for the degrees of freedom of the submodular regularization estimators. It is
Kentaro Minami
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UNSURE: self-supervised learning with Unknown Noise level and Stein's Unbiased Risk Estimate [PDF]
Recently, many self-supervised learning methods for image reconstruction have been proposed that can learn from noisy data alone, bypassing the need for ground-truth references. Most existing methods cluster around two classes: i) Stein's Unbiased Risk Estimate (SURE) and similar approaches that assume full knowledge of the noise distribution, and ii ...
Julián Tachella +2 more
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On the “degrees of freedom” of the lasso [PDF]
, 2007 We study the effective degrees of freedom of the lasso in the framework of Stein’s unbiased risk estimation (SURE). We show that the number of nonzero coefficients is an unbiased estimate forthe degrees of freedom of the lasso—a conclusion that requires ...
H. Zou, T. Hastie, R. Tibshirani
semanticscholar +3 more sources
Indian Journal of Science and Technology, 2016 Objective: The main objective of this research is to preserve the edges and also remove the noise from high dynamic range videos during filtering process.
P. Kavitha, A. Vijendran
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