An IHT Algorithm for Sparse Recovery From Subexponential Measurements Academic Article

abstract

• 1994-2012 IEEE. A matrix whose entries are independent subexponential random variables is not likely to satisfy the classical restricted isometry property in the optimal regime of parameters. However, it is known that uniform sparse recovery is still possible with high probability in the optimal regime if ones uses 1-minimization as a recovery algorithm. We show in this letter that such a statement remains valid if one uses a new variation of iterative hard thresholding as a recovery algorithm. The argument is based on a modified restricted isometry property featuring the 1-norm as the inner norm.

authors

• Foucart, Simon

published proceedings

• IEEE SIGNAL PROCESSING LETTERS

author list (cited authors)

• Foucart, S., & Lecue, G.

citation count

• 8

complete list of authors

• Foucart, Simon||Lecue, Guillaume

publication date

• September 2017

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• IEEE Signal Processing Letters  Journal

keywords

• Compressive Sensing
• Iterative Hard Thresholding
• Restricted Isometry Property
• Sparse Recovery
• Subexponential Random Variable

Digital Object Identifier (DOI)

• 10.1109/LSP.2017.2721500

start page

• 1280

end page

• 1283

volume

• 24

issue

• 9

URL

• http://dx.doi.org/10.1109/lsp.2017.2721500