RT Journal Article T1 The noise collector for sparse recovery in high dimensions A1 Moscoso, Miguel A1 Novikov, Alexei A1 Papanicolaou, George A1 Tsogka, Chrysoula AB 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 Alpharho=b0 can be found efficiently with an l1-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 tau and the Noise Collector matrix C and solve an augmented system Alpharho+Ceta=b0+e, where e is the noise. We show that the l1-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 b0 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. PB National Academy of Sciences SN 1091-6490 SN 0027-8424 (online) YR 2020 FD 2020-05-26 LK https://hdl.handle.net/10016/32485 UL https://hdl.handle.net/10016/32485 LA eng NO The work of M.M. was partially supported by Spanish Ministerio de Ciencia e InnovaciĆ³n Grant FIS2016-77892-R. The work of A.N. was partially supported by NSF Grants DMS-1515187 and DMS-1813943. The work of G.P. was partially supported by Air Force Office of Scientific Research (AFOSR) Grant FA9550-18-1-0519. The work of C.T. was partially supported by AFOSR Grants FA9550-17-1-0238 and FA9550-18-1-0519. DS e-Archivo RD 26 jun. 2024