Soft recovery

Axel Flinth

doi:10.1117/12.2272132

24 August 2017 l_1,2-minimization and soft recovery

Axel Flinth

Proceedings Volume 10394, Wavelets and Sparsity XVII; 103940U (2017) https://doi.org/10.1117/12.2272132
Event: SPIE Optical Engineering + Applications, 2017, San Diego, California, United States

Abstract

This article provides a new type of analysis of a compressed-sensing based technique for recovering column-sparse matrices, namely minimization of the l_1,2-norm. Rather than providing conditions on the measurement matrix which guarantees the solution of the program to be exactly equal to the ground truth signal (which already has been thoroughly investigated), it presents a condition which guarantees that the solution is approximately equal to the ground truth. Soft recovery statements of this kind are to the best knowledge of the author a novelty in Compressed Sensing.

Citation Download Citation

Axel Flinth "l_1,2-minimization and soft recovery", Proc. SPIE 10394, Wavelets and Sparsity XVII, 103940U (24 August 2017); https://doi.org/10.1117/12.2272132

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