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a The recovery error.
With increased SNR, the recovery error is obviously decreased.
We will take advantage of the recovery error (|x-hat{x}|) to evaluate our algorithm.
The recovery error and running time were introduced for quantitative evaluation.
But the performance is still far from unsatisfactory, considering the resulting high recovery error and complexity.
The following theorems present our results on the recovery error using ℓ1-min decoding of (28).
More specifically, this example considers two possible signal recovery problems described in detail in Section 5.1 and characterizes performance in terms of two metrics: the root mean square signal recovery error (RMSE) and the mean absolute signal recovery error (MAE).
Two performance measures are considered for both problems: the root-mean-square recovery error (RMSE) is more widely used, but may be less appropriate than the mean absolute recovery error (MAE) in the presence of impulsive noise.
It also allows us to simplify the mathematical analysis to provide a theoretical bound on the resulting recovery error.
Unless the non-uniform samples capture the behavior of these short-duration transients, a larger recovery error is achieved.
Hence, the Gaussian basis typically requires higher OSR than the other two bases for the same recovery error.
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