OptShrink

swMATH33657MaRDI QIDQ45366

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• OptShrink: An Algorithm for Improved Low-Rank Signal Matrix Denoising by Optimal, Data-Driven Singular Value Shrinkage

Related Items (23)

Heteroskedastic PCA: algorithm, optimality, and applications ⋮ Optimally Weighted PCA for High-Dimensional Heteroscedastic Data ⋮ Adaptive singular value shrinkage estimate for low rank tensor denoising ⋮ Efficient computation of limit spectra of sample covariance matrices ⋮ Asymptotic performance of PCA for high-dimensional heteroscedastic data ⋮ Optimal prediction in the linearly transformed spiked model ⋮ Optimal singular value shrinkage for operator norm loss: extending to non-square matrices ⋮ Estimating latent asset-pricing factors ⋮ Testing in high-dimensional spiked models ⋮ Time Series Source Separation Using Dynamic Mode Decomposition ⋮ Matrix completion for matrices with low-rank displacement ⋮ Permutation methods for factor analysis and PCA ⋮ \(e\)PCA: high dimensional exponential family PCA ⋮ Rapid evaluation of the spectral signal detection threshold and Stieltjes transform ⋮ Matrix Denoising for Weighted Loss Functions and Heterogeneous Signals ⋮ MIXANDMIX: numerical techniques for the computation of empirical spectral distributions of population mixtures ⋮ High dimensional deformed rectangular matrices with applications in matrix denoising ⋮ Spiked separable covariance matrices and principal components ⋮ Generalized SURE for optimal shrinkage of singular values in low-rank matrix denoising ⋮ Low-rank matrix denoising for count data using unbiased Kullback-Leibler risk estimation ⋮ Edge statistics of large dimensional deformed rectangular matrices ⋮ Deterministic Parallel Analysis: An Improved Method for Selecting Factors and Principal Components ⋮ Matrix estimation by universal singular value thresholding

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