On an unsupervised method for parameter selection for the elastic net

DOI10.3934/mine.2022053OpenAlexW3216496702MaRDI QIDQ2167641

Zeljko Kereta, Valeriya Naumova

Publication date: 25 August 2022

Published in: Mathematics in Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.3934/mine.2022053

zbMATH Keywords

parameter selectioniterative thresholdingdata-driven regularizationmatrix concentration inequalitiessub-Gaussian vectorselastic net regularization

Mathematics Subject Classification ID

Statistics (62-XX) Computer science (68-XX)

Cites Work

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• The projected GSURE for automatic parameter tuning in iterative shrinkage methods
• Comparing parameter choice methods for regularization of ill-posed problems
• Optimal order of convergence and (in)tractability of multivariate approximation of smooth functions
• Elastic-net regularization in learning theory
• Estimation of the mean of a multivariate normal distribution
• Image recovery via total variation minimization and related problems
• False discoveries occur early on the Lasso path
• Nonlinear GCV and quasi-GCV for shrinkage models
• Least angle regression. (With discussion)
• Adaptive kernel methods using the balancing principle
• Morozov's discrepancy principle and Tikhonov-type functionals
• Elastic-net regularization: error estimates and active set methods
• Generalized Cross-Validation as a Method for Choosing a Good Ridge Parameter
• Analysis of Discrete Ill-Posed Problems by Means of the L-Curve
• Ideal spatial adaptation by wavelet shrinkage
• Nonlinear wavelet image processing: variational problems, compression, and noise removal through wavelet shrinkage
• Generalized SURE for Exponential Families: Applications to Regularization
• High-Dimensional Probability
• On a Problem of Adaptive Estimation in Gaussian White Noise
• De-noising by soft-thresholding
• The use of monotonicity for choosing the regularization parameter in ill-posed problems
• A machine learning approach to optimal Tikhonov regularization I: Affine manifolds
• Stein Unbiased GrAdient estimator of the Risk (SUGAR) for Multiple Parameter Selection
• Morozov's discrepancy principle for Tikhonov-type functionals with nonlinear operators
• Regularization and Variable Selection Via the Elastic Net
• An Introduction to Matrix Concentration Inequalities
• Use of the regularization method in non-linear problems

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