Recent work in the area of compressed sensing mainly focuses on the perfect recovery of the entire support for sparse signals. However, partial support recovery, where a part of the signal support is correctly recover...
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Recent work in the area of compressed sensing mainly focuses on the perfect recovery of the entire support for sparse signals. However, partial support recovery, where a part of the signal support is correctly recovered, may be adequate in many practical scenarios. In this study, in the high-dimensional and noisy setting, the authors develop the probability of partial support recovery of the optimalmaximum-likelihood (ML) algorithm. When a large part of the support is available, the asymptotic mean-square-error (MSE) of the reconstructed signal is further developed. The simulation results characterise the asymptotic performance of the ML algorithm for partial support recovery, and show that there exists a signal-to-noise ratio (SNR) threshold, beyond which the increase of SNR cannot bring any obvious MSE gain.
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