New robust regularized shrinkage regression for high-dimensional image recovery and alignment via affine transformation and Tikhonov regularization

DOI10.1155/2020/1286909zbMath1486.62211OpenAlexW3096193736MaRDI QIDQ2224700

Wen Xian, Xuan Tang, Habte Tadesse Likassa

Publication date: 4 February 2021

Published in: International Journal of Mathematics and Mathematical Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2020/1286909

zbMATH Keywords

Tikhonov regularizationregression method

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Ridge regression; shrinkage estimators (Lasso) (62J07) Nonparametric robustness (62G35) Numerical mathematical programming methods (65K05) Convex programming (90C25) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Related Items (1)

New robust PCA for outliers and heavy sparse noises' detection via affine transformation, the \(L_{\ast, w}\) and \(L_{2,1}\) norms, and spatial weight matrix in high-dimensional images: from the perspective of signal processing

Uses Software

• FSIM
• LFW
• tproduct
• RASL

Cites Work

• Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
• New robust principal component analysis for joint image alignment and recovery via affine transformations, Frobenius and \(L_{2,1}\) norms
• A Novel M-Estimator for Robust PCA
• Outlier-Robust PCA: The High-Dimensional Case
• Robust principal component analysis?
• A Singular Value Thresholding Algorithm for Matrix Completion
• Tensor Factorization for Low-Rank Tensor Completion
• Robust PCA Based on Incoherence With Geometrical Interpretation
• Robust and Low-Rank Representation for Fast Face Identification With Occlusions
• Analysis of Distributed ADMM Algorithm for Consensus Optimization in Presence of Node Error
• FSIM: A Feature Similarity Index for Image Quality Assessment

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