Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Accelerated Sparse Bayesian Learning via Screening Test and Its
  Applications   In high-dimensional settings, sparse structures are critical for efficiency
in term of memory and computation complexity. For a linear system, to find the
sparsest solution provided with an over-complete dictionary of features
directly is typically NP-hard, and thus alternative approximate methods should
be considered. In this paper, our choice for alternative method is sparse
Bayesian learning, which, as empirical Bayesian approaches, uses a
parameterized prior to encourage sparsity in solution, rather than the other
methods with fixed priors such as LASSO. Screening test, however, aims at
quickly identifying a subset of features whose coefficients are guaranteed to
be zero in the optimal solution, and then can be safely removed from the
complete dictionary to obtain a smaller, more easily solved problem. Next, we
solve the smaller problem, after which the solution of the original problem can
be recovered by padding the smaller solution with zeros. The performance of the
proposed method will be examined on various data sets and applications.

30
The categories of this paper are: q-alg
10
The categories of this paper are: q-bio
7
The categories of this paper are: hep-th
23
The categories of this paper are: solv-int
34
The categories of this paper are: chem-ph
22
The categories of this paper are: chao-dyn
32
The categories of this paper are: atom-ph
13
The categories of this paper are: quant-ph
14
The categories of this paper are: math-ph
36
The categories of this paper are: cmp-lg
21
The categories of this paper are: adap-org
8
The categories of this paper are: hep-ex
24
The categories of this paper are: dg-ga
9
The categories of this paper are: nlin
0
The categories of this paper are: hep-ph
37
The categories of this paper are: supr-con