1,544 research outputs found
Compressed sensing of data with a known distribution
Compressed sensing is a technique for recovering an unknown sparse signal
from a small number of linear measurements. When the measurement matrix is
random, the number of measurements required for perfect recovery exhibits a
phase transition: there is a threshold on the number of measurements after
which the probability of exact recovery quickly goes from very small to very
large. In this work we are able to reduce this threshold by incorporating
statistical information about the data we wish to recover. Our algorithm works
by minimizing a suitably weighted -norm, where the weights are chosen
so that the expected statistical dimension of the corresponding descent cone is
minimized. We also provide new discrete-geometry-based Monte Carlo algorithms
for computing intrinsic volumes of such descent cones, allowing us to bound the
failure probability of our methods.Comment: 22 pages, 7 figures. New colorblind safe figures. Sections 3 and 4
completely rewritten. Minor typos fixe
- …