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We investigate recovery of nonnegative vectors from non-adaptive compressive measurements in the presence of noise of unknown power.
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2012
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R. Vershynin, “Introduction to the non-asymptotic analysis of random matrices,” in
2012
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N. Meinshausen, “Sign-constrained least squares estimation for high-dimensional regression,”
2013
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S. Dirksen, G. Lecue, and H. Rauhut, “On the gap between restricted isometry properties and sparse recovery conditions,”
2016
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Y. Chang, P. Jung, C. Zhou, and S. Stanczak, “Block compressed sensing based distributed resource allocation for m2m communications,” in
2016
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2016
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R. Kueng and J. P., “Robust Nonnegative Sparse Recovery and 0/1-Bernoulli Measurements,” in
2016
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