Confidence Region of Singular Subspaces for Low-Rank Matrix Regression

DOI10.1109/TIT.2019.2924900zbMath1433.94034arXiv1805.09871OpenAlexW2955299163WikidataQ127524100 ScholiaQ127524100MaRDI QIDQ5211515

Dong Xia

Publication date: 28 January 2020

Published in: IEEE Transactions on Information Theory (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1805.09871

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Central limit and other weak theorems (60F05) Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

Related Items (5)

Inference for low-rank tensors -- no need to debias ⋮ Singular vector and singular subspace distribution for the matrix denoising model ⋮ Asymmetry helps: eigenvalue and eigenvector analyses of asymmetrically perturbed low-rank matrices ⋮ Normal approximation and confidence region of singular subspaces ⋮ A novel robust principal component analysis algorithm of nonconvex rank approximation

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