Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Optimal feature selection for sparse linear discriminant analysis and
  its applications in gene expression data   This work studies the theoretical rules of feature selection in linear
discriminant analysis (LDA), and a new feature selection method is proposed for
sparse linear discriminant analysis. An $l_1$ minimization method is used to
select the important features from which the LDA will be constructed. The
asymptotic results of this proposed two-stage LDA (TLDA) are studied,
demonstrating that TLDA is an optimal classification rule whose convergence
rate is the best compared to existing methods. The experiments on simulated and
real datasets are consistent with the theoretical results and show that TLDA
performs favorably in comparison with current methods. Overall, TLDA uses a
lower minimum number of features or genes than other approaches to achieve a
better result with a reduced misclassification rate.

4
The categories of this paper are: cond-mat
37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
24
The categories of this paper are: dg-ga
28
The categories of this paper are: alg-geom
33
The categories of this paper are: plasm-ph
36
The categories of this paper are: cmp-lg
31
The categories of this paper are: ao-sci
5
The categories of this paper are: gr-qc
8
The categories of this paper are: hep-ex
10
The categories of this paper are: q-bio
22
The categories of this paper are: chao-dyn
1
The categories of this paper are: cs
34
The categories of this paper are: chem-ph
14
The categories of this paper are: math-ph
25
The categories of this paper are: patt-sol