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In this paper, we consider robust system identification under sparse outliers and random noises. In our problem, system parameters are observed through a Toeplitz matrix. All observations are subject to random noises and a few are corrupted with outliers. We reduce this problem of system identification to a sparse error correcting problem using a Toeplitz structured real-numbered codingmatrix. We...
An unknown vector f in Rn can be recovered from corrupted measurements y = Af + e where Am×n(m ≥ n) is the coding matrix if the unknown error vector e is sparse. We investigate the relationship of the fraction of errors and the recovering ability of lp-minimization (0 <; p ≤ 1) which returns a vector x that minimizes the "lp-norm" of y-Ax. We give sharp thresholds of the fraction of errors...
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