A field known as Compressive Sensing (CS) has recently emerged to help
address the growing challenges of capturing and processing high-dimensional
signals and data sets. CS exploits the surprising fact that the information
contained in a sparse signal can be preserved in a small number of compressive
(or random) linear measurements of that signal. Strong theoretical guarantees
have been established on the accuracy to which sparse or near-sparse signals
can be recovered from noisy compressive measurements.