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
DOI10.1155/2021/3047712zbMath1483.68436OpenAlexW3204636424MaRDI QIDQ2238436
Peidong Liang, Chentao Zhang, Habte Tadesse Likassa, Jielong Guo
Publication date: 1 November 2021
Published in: International Journal of Mathematics and Mathematical Sciences (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1155/2021/3047712
Factor analysis and principal components; correspondence analysis (62H25) Numerical mathematical programming methods (65K05) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) Machine vision and scene understanding (68T45)
Uses Software
Cites Work
- Unnamed Item
- Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
- Decomposition into low-rank plus additive matrices for background/foreground separation: a review for a comparative evaluation with a large-scale dataset
- New robust principal component analysis for joint image alignment and recovery via affine transformations, Frobenius and \(L_{2,1}\) norms
- New robust regularized shrinkage regression for high-dimensional image recovery and alignment via affine transformation and Tikhonov regularization
- Generalized principal component analysis
- Recovering Low-Rank and Sparse Components of Matrices from Incomplete and Noisy Observations
- Robust principal component analysis?
- Dynamical Textures Modeling via Joint Video Dictionary Learning
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