Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published December 2010 | metadata_only
Journal Article

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.

Additional Information

© 2010 ACM. The original version of this paper appeared in Applied and Computational Harmonic Analysis 26, 3 (2008), 301–321.

Additional details

August 19, 2023
August 19, 2023