Work

Improved bounds for sparse recovery from adaptive measurements

Year: 2010

Type: conference-paper

Abstract: It is shown here that adaptivity in sampling results in dramatic improvements in the recovery of sparse signals in white Gaussian noise. An adaptive sampling-and-refinement procedure called distilled sensing is discussed and analyzed, resulting in fundamental new asymptotic scaling relationships in terms of the minimum feature strength required for reliable signal detection or localization (support recovery). In particular, reliable detection and localization using non-adaptive samples is possible only if the feature strength grows logarithmically in the problem dimension. Here it is shown that using adaptive sampling, reliable detection is possible provided the feature strength exceeds a constant, and localization is possible when the feature strength exceeds any (arbitrarily slowly) growing function of the problem dimension.

Source: IEEE International Symposium on Information Theory (ISIT)

DOI: 10.1109/isit.2010.5513489

Authors: Jarvis Haupt, Rui Castro, Robert Nowak

Institutions: Rice University, Columbia University, University of Wisconsin–Madison

Language: English

Topic: Sparse and Compressive Sensing Techniques

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Air Force Office of Scientific Research

Awards: G1523888516, G201076361