Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Bayesian compressed sensing with new sparsity-inducing prior   Sparse Bayesian learning (SBL) is a popular approach to sparse signal
recovery in compressed sensing (CS). In SBL, the signal sparsity information is
exploited by assuming a sparsity-inducing prior for the signal that is then
estimated using Bayesian inference. In this paper, a new sparsity-inducing
prior is introduced and efficient algorithms are developed for signal recovery.
The main algorithm is shown to produce a sparser solution than existing SBL
methods while preserving their desirable properties. Numerical simulations with
one-dimensional synthetic signals and two-dimensional images verify our
analysis and show that for sparse signals the proposed algorithm outperforms
its SBL peers in both the signal recovery accuracy and computational speed. Its
improved performance is also demonstrated in comparison with other
state-of-the-art methods in CS.

27
The categories of this paper are: comp-gas
22
The categories of this paper are: chao-dyn
5
The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg
15
The categories of this paper are: nucl-ex
33
The categories of this paper are: plasm-ph
23
The categories of this paper are: solv-int
24
The categories of this paper are: dg-ga
34
The categories of this paper are: chem-ph
3
The categories of this paper are: physics
13
The categories of this paper are: quant-ph
17
The categories of this paper are: q-fin
28
The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th
14
The categories of this paper are: math-ph
21
The categories of this paper are: adap-org