Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Computationally Efficient Sparse Bayesian Learning via Generalized
  Approximate Message Passing   The sparse Beyesian learning (also referred to as Bayesian compressed
sensing) algorithm is one of the most popular approaches for sparse signal
recovery, and has demonstrated superior performance in a series of experiments.
Nevertheless, the sparse Bayesian learning algorithm has computational
complexity that grows exponentially with the dimension of the signal, which
hinders its application to many practical problems even with moderately large
data sets. To address this issue, in this paper, we propose a computationally
efficient sparse Bayesian learning method via the generalized approximate
message passing (GAMP) technique. Specifically, the algorithm is developed
within an expectation-maximization (EM) framework, using GAMP to efficiently
compute an approximation of the posterior distribution of hidden variables. The
hyperparameters associated with the hierarchical Gaussian prior are learned by
iteratively maximizing the Q-function which is calculated based on the
posterior approximation obtained from the GAMP. Numerical results are provided
to illustrate the computational efficacy and the effectiveness of the proposed
algorithm.

3
The categories of this paper are: physics
14
The categories of this paper are: math-ph
20
The categories of this paper are: acc-phys
9
The categories of this paper are: nlin
35
The categories of this paper are: mtrl-th
21
The categories of this paper are: adap-org
34
The categories of this paper are: chem-ph
8
The categories of this paper are: hep-ex
19
The categories of this paper are: eess
26
The categories of this paper are: bayes-an
5
The categories of this paper are: gr-qc
33
The categories of this paper are: plasm-ph
15
The categories of this paper are: nucl-ex
27
The categories of this paper are: comp-gas
36
The categories of this paper are: cmp-lg
17
The categories of this paper are: q-fin