One sketch for all: fast algorithms for compressed sensing

Compressed Sensing is a new paradigm for acquiring the compressible signals that arise in many applications. These signals can be approximated using an amount of information much smaller than the nominal dimension of the signal. Traditional approaches acquire the entire signal and process it to extract the information. The new approach acquires a small number of nonadaptive linear measurements of the signal and uses sophisticated algorithms to determine its information content. Emerging technologies can compute these general linear measurements of a signal at unit cost per measurement. This paper exhibits a randomized measurement ensemble and a signal reconstruction algorithm that satisfy four requirements:

• Type: Conference
• Year: 2007
• Where: STOC
• Authors: Anna C. Gilbert, Martin J. Strauss, Joel A. Tropp, Roman Vershynin
• Keywords: Algorithms, Entire Signal, Nonadaptive Linear Measurements, Signal Reconstruction Algorithm, STOC 2007