Low-rank Matrix Recovery from Noisy, Quantized and Erroneous Measurements

DOI10.1109/TSP.2018.2821648zbMATH Open1415.94109OpenAlexW2795744451WikidataQ130043969 ScholiaQ130043969MaRDI QIDQ4622177

Meng Wang, Ren Wang, Pengzhi Gao, Joe Hong Chow

Publication date: 12 February 2019

Published in: IEEE Transactions on Signal Processing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1109/tsp.2018.2821648

Mathematics Subject Classification ID

Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

Related Items (6)

Low rank matrix recovery from rank one measurements ⋮ Recovering Low-Rank and Sparse Components of Matrices from Incomplete and Noisy Observations ⋮ Estimation of (near) low-rank matrices with noise and high-dimensional scaling ⋮ Recovering low-rank matrices from binary measurements ⋮ Quantization for low-rank matrix recovery ⋮ LOW-RANK AND SPARSE MATRIX RECOVERY FROM NOISY OBSERVATIONS VIA 3-BLOCK ADMM ALGORITHM

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