Adaptive singular value shrinkage estimate for low rank tensor denoising
From MaRDI portal
Publication:5041692
DOI10.1142/S2010326322500381zbMath1496.62097OpenAlexW4224032990WikidataQ114071591 ScholiaQ114071591MaRDI QIDQ5041692
Publication date: 14 October 2022
Published in: Random Matrices: Theory and Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1142/s2010326322500381
Factor analysis and principal components; correspondence analysis (62H25) Estimation in multivariate analysis (62H12) Image analysis in multivariate analysis (62H35) Empirical decision procedures; empirical Bayes procedures (62C12)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Tensor Decompositions and Applications
- Tensor-Train Decomposition
- The Adaptive Lasso and Its Oracle Properties
- Bi-cross-validation of the SVD and the nonnegative matrix factorization
- Projected estimation for large-dimensional matrix factor models
- Adaptive shrinkage of singular values
- Reconstruction of a low-rank matrix in the presence of Gaussian noise
- Factorization strategies for third-order tensors
- Tensor-tensor products with invertible linear transforms
- Estimation of the mean of a multivariate normal distribution
- Factor models for matrix-valued high-dimensional time series
- Community detection on mixture multilayer networks via regularized tensor decomposition
- Estimation of Kullback-Leibler losses for noisy recovery problems within the exponential family
- Adaptive higher-order spectral estimators
- On the ``degrees of freedom of the lasso
- The Optimal Hard Threshold for Singular Values is <inline-formula> <tex-math notation="TeX">\(4/\sqrt {3}\) </tex-math></inline-formula>
- OptShrink: An Algorithm for Improved Low-Rank Signal Matrix Denoising by Optimal, Data-Driven Singular Value Shrinkage
- Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions
- Tensor completion and low-n-rank tensor recovery via convex optimization
- Tensor SVD: Statistical and Computational Limits
- Generalized SURE for Exponential Families: Applications to Regularization
- Unbiased Risk Estimates for Singular Value Thresholding and Spectral Estimators
- Efficient Tensor Completion for Color Image and Video Recovery: Low-Rank Tensor Train
- Exact Tensor Completion Using t-SVD
- SURE Estimates for a Heteroscedastic Hierarchical Model
- An Order-$p$ Tensor Factorization with Applications in Imaging
- SURE estimates for high dimensional classification
- Efficient empirical Bayes estimates for risk parameters of Pareto distributions
- Optimal Sparse Singular Value Decomposition for High-Dimensional High-Order Data
- Tensor Regression with Applications in Neuroimaging Data Analysis
- Empirical Bayes on vector observations: An extension of Stein's method
- The Estimation of Prediction Error
This page was built for publication: Adaptive singular value shrinkage estimate for low rank tensor denoising
