Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Communication-efficient Distributed Sparse Linear Discriminant Analysis   We propose a communication-efficient distributed estimation method for sparse
linear discriminant analysis (LDA) in the high dimensional regime. Our method
distributes the data of size $N$ into $m$ machines, and estimates a local
sparse LDA estimator on each machine using the data subset of size $N/m$. After
the distributed estimation, our method aggregates the debiased local estimators
from $m$ machines, and sparsifies the aggregated estimator. We show that the
aggregated estimator attains the same statistical rate as the centralized
estimation method, as long as the number of machines $m$ is chosen
appropriately. Moreover, we prove that our method can attain the model
selection consistency under a milder condition than the centralized method.
Experiments on both synthetic and real datasets corroborate our theory.

17
The categories of this paper are: q-fin
7
The categories of this paper are: hep-th
2
The categories of this paper are: math
36
The categories of this paper are: cmp-lg
31
The categories of this paper are: ao-sci
20
The categories of this paper are: acc-phys
8
The categories of this paper are: hep-ex
4
The categories of this paper are: cond-mat
21
The categories of this paper are: adap-org
14
The categories of this paper are: math-ph
24
The categories of this paper are: dg-ga
1
The categories of this paper are: cs
32
The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
10
The categories of this paper are: q-bio
9
The categories of this paper are: nlin