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Nonconvex Compressed Sensing and Error Correction Export

Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on, Vol. 3 (04 June 2007), pp. III-889-III-892.

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The theory of compressed sensing has shown that sparse signals can be reconstructed exactly from remarkably few measurements. In this paper we consider a nonconvex extension, where the lscr<sup>1</sup>1 norm of the basis pursuit algorithm is replaced with the lscr<sub>p</sub> norm, for p < 1. In the context of sparse error correction, we perform numerical experiments that show that for a fixed number of measurements, errors of larger support can be corrected in the nonconvex case. We also provide a theoretical justification for why this should be so.


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