Jan 8, 2013 · Here we analyze the typical performance of an L1-norm based signal recovery scheme for the 1-bit compressed sensing using statistical mechanics methods.
Here, we analyze the typical performance of an l 1 -norm-based signal recovery scheme for 1-bit compressed sensing using statistical mechanics methods.
Compressed sensing is a framework that makes it possible to recover an N-dimensional sparse vector x∈R N from its linear transformation y∈R M of lower ...
Compressed sensing is a framework that makes it possible to recover an N-dimensional sparse vector x∈RN from its linear transformation y∈RM of lower ...
Bayesian signal reconstruction for 1-bit compressed sensing · Compressed sensing with ℓ0-norm: statistical physics analysis & algorithms for signal recovery.
Feb 15, 2014 · Abstract. Compressed sensing is a framework that makes it possible to recover an N-dimensional sparse vector x ∈ RN from its linear ...
In this paper, the problem of one-bit compressed sensing (OBCS) is formulated as a problem in probably approximately correct (PAC) learning. It is shown that ...
Abstract. In this paper, the problem of one-bit compressed sensing (OBCS) is formulated as a problem in probably approximately correct (PAC) learning.
Missing: mechanics | Show results with:mechanics
The one-bit compressed sensing framework aims to reconstruct a sparse signal by only using the sign information of its linear measurements.
In this paper we consider the limiting case of 1-bit measurements, which preserve only the sign information of the random measurements. Although it is possible.