CoSaMP: Iterative Signal Recovery from Incomplete and Inaccurate Samples
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.
© 2010 ACM
"CoSaMP: Iterative signal recovery from incomplete and inaccurate samples" by D. Needell and J. A. Tropp Extended Abstract, Communications of the ACM, "Research Highlights" section, Dec. 2010.