Key points are not available for this paper at this time.
In this paper, we study the number of measurements required to recover a sparse signal in CM with L non-zero coefficients from compressed samples in the presence of noise. For a number of different recovery criteria, we prove that O (L) (an asymptotically linear multiple of L) measurements are necessary and sufficient if L grows linearly as a function of M. This improves on the existing literature that is mostly focused on variants of a specific recovery algorithm based on convex programming, for which O (L (M-L) ) measurements are required. We also show that O (L (M-L) ) measurements are required in the sublinear regime (L = o (M) ).
Akçakaya et al. (Fri,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: