An efficient approach for computing optimal low-rank regularized inverse matrices

DOI10.1088/0266-5611/30/11/114009zbMath1305.65130arXiv1404.1610OpenAlexW2139257007MaRDI QIDQ2936503

Matthias Chung, Julianne Chung

Publication date: 17 December 2014

Published in: Inverse Problems (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1404.1610

zbMATH Keywords

regularizationnumerical experimenttruncated singular value decompositionmachine learningBayes risklow-rank approximationill-posed inverse problemsempirical Bayes risk

Mathematics Subject Classification ID

Ill-posedness and regularization problems in numerical linear algebra (65F22)

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Optimal Low-rank Approximations of Bayesian Linear Inverse Problems ⋮ Low-Rank Eigenvector Compression of Posterior Covariance Matrices for Linear Gaussian Inverse Problems ⋮ Learning regularization parameters for general-form Tikhonov ⋮ Goal-Oriented Optimal Approximations of Bayesian Linear Inverse Problems ⋮ Effective implementation to reduce execution time of a low-rank matrix approximation problem ⋮ A general class of arbitrary order iterative methods for computing generalized inverses ⋮ Optimal regularized low rank inverse approximation ⋮ Regularized Computation of Approximate Pseudoinverse of Large Matrices Using Low-Rank Tensor Train Decompositions ⋮ Efficient Marginalization-Based MCMC Methods for Hierarchical Bayesian Inverse Problems ⋮ Optimal Regularized Inverse Matrices for Inverse Problems

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