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Title: The Noise Collector for sparse recovery in high dimensions
The ability to detect sparse signals from noisy, high-dimensional data is a top priority in modern science and engineering. It is well known that a sparse solution of the linear system A ρ = b 0 can be found efficiently with an ℓ 1 -norm minimization approach if the data are noiseless. However, detection of the signal from data corrupted by noise is still a challenging problem as the solution depends, in general, on a regularization parameter with optimal value that is not easy to choose. We propose an efficient approach that does not require any parameter estimation. We introduce a no-phantom weight τ and the Noise Collector matrix C and solve an augmented system A ρ + C η = b 0 + e , where e is the noise. We show that the ℓ 1 -norm minimal solution of this system has zero false discovery rate for any level of noise, with probability that tends to one as the dimension of b 0 increases to infinity. We obtain exact support recovery if the noise is not too large and develop a fast Noise Collector algorithm, which makes the computational cost of solving the augmented system comparable with that of the original one. We demonstrate the effectiveness of the method in applications to passive array imaging.  more » « less
Award ID(s):
1813943
PAR ID:
10163271
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Proceedings of the National Academy of Sciences
Volume:
117
Issue:
21
ISSN:
0027-8424
Page Range / eLocation ID:
11226 to 11232
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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