CoSaMP: iterative signal recovery from incomplete and inaccurate samples

Authors:
Deanna Needell;Joel A. Tropp
Affiliations:
Stanford University, Stanford, CA;California Institute of Technology, Pasadena, CA
Venue:
Communications of the ACM
Year:
2010

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Abstract

Compressive sampling (CoSa) is a new paradigm for developing data sampling technologies. It is based on the principle that many types of vector-space data are compressible, which is a term of art in mathematical signal processing. The key ideas are that randomized dimension reduction preserves the information in a compressible signal and that it is possible to develop hardware devices that implement this dimension reduction efficiently. The main computational challenge in CoSa is to reconstruct a compressible signal from the reduced representation acquired by the sampling device. This extended abstract describes a recent algorithm, called, CoSaMP, that accomplishes the data recovery task. It was the first known method to offer near-optimal guarantees on resource usage.