Recovering low-rank matrices from binary measurements

DOI10.3934/ipi.2019032zbMath1431.15002OpenAlexW2945209458WikidataQ127797117 ScholiaQ127797117MaRDI QIDQ2316352

Simon Foucart, Richard G. Lynch

Publication date: 26 July 2019

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

Full work available at URL: https://doi.org/10.3934/ipi.2019032

zbMATH Keywords

semidefinite programmingadaptivityquantizationone-bit compressive sensinghard singular value thresholdinglow-rank recovery

Mathematics Subject Classification ID

Semidefinite programming (90C22) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Vector spaces, linear dependence, rank, lineability (15A03)

Related Items (4)

Iterative hard thresholding for low-rank recovery from rank-one projections ⋮ Robust one-bit compressed sensing with partial circulant matrices ⋮ Quantized Compressed Sensing: A Survey ⋮ Endpoint Results for Fourier Integral Operators on Noncompact Symmetric Spaces

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