Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Optimal variable selection in multi-group sparse discriminant analysis   This article considers the problem of multi-group classification in the
setting where the number of variables $p$ is larger than the number of
observations $n$. Several methods have been proposed in the literature that
address this problem, however their variable selection performance is either
unknown or suboptimal to the results known in the two-group case. In this work
we provide sharp conditions for the consistent recovery of relevant variables
in the multi-group case using the discriminant analysis proposal of Gaynanova
et al., 2014. We achieve the rates of convergence that attain the optimal
scaling of the sample size $n$, number of variables $p$ and the sparsity level
$s$. These rates are significantly faster than the best known results in the
multi-group case. Moreover, they coincide with the optimal minimax rates for
the two-group case. We validate our theoretical results with numerical
analysis.

17
The categories of this paper are: q-fin
6
The categories of this paper are: astro-ph
1
The categories of this paper are: cs
32
The categories of this paper are: atom-ph
12
The categories of this paper are: nucl-th
4
The categories of this paper are: cond-mat
19
The categories of this paper are: eess
9
The categories of this paper are: nlin
7
The categories of this paper are: hep-th
25
The categories of this paper are: patt-sol
10
The categories of this paper are: q-bio
37
The categories of this paper are: supr-con
14
The categories of this paper are: math-ph
31
The categories of this paper are: ao-sci
28
The categories of this paper are: alg-geom
33
The categories of this paper are: plasm-ph