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• W2963487846 abstract "We investigate recovery of nonnegative vectors from non-adaptive compressive measurements in the presence of noise of unknown power. In the absence of noise, existing results in the literature identify properties of the measurement that assure uniqueness in the non-negative orthant. By linking such uniqueness results to nullspace properties, we deduce uniform and robust compressed sensing guarantees for nonnegative least squares. No ℒ <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>1</sub> -regularization is required. As an important proof of principle, we establish that m × n random i.i.d. 0/1-valued Bernoulli matrices obey the required conditions with overwhelming probability provided that m = O(s log(n/s)). We achieve this by establishing the robust nullspace property for random 0/1-matrices-a novel result in its own right. Our analysis is motivated by applications in wireless network activity detection." @default.
• W2963487846 created "2019-07-30" @default.
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• W2963487846 date "2018-02-01" @default.
• W2963487846 modified "2023-10-02" @default.
• W2963487846 title "Robust Nonnegative Sparse Recovery and the Nullspace Property of 0/1 Measurements" @default.
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• W2963487846 doi "https://doi.org/10.1109/tit.2017.2746620" @default.
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