A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is
Compressive distilled sensing: Sparse recovery using adaptivity in compressive measurements
2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers
The recently-proposed theory of distilled sensing establishes that adaptivity in sampling can dramatically improve the performance of sparse recovery in noisy settings. In particular, it is now known that adaptive point sampling enables the detection and/or support recovery of sparse signals that are otherwise too weak to be recovered using any method based on non-adaptive point sampling. In this paper the theory of distilled sensing is extended to highly-undersampled regimes, as in compressivedoi:10.1109/acssc.2009.5470138 fatcat:dcdqxzq2nvf3javspjyrh2iysq