Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Feature Screening: Feature Selection for Ultra High-Dimensional
  Data via Deep Neural Networks   The applications of traditional statistical feature selection methods to
high-dimension, low sample-size data often struggle and encounter challenging
problems, such as overfitting, curse of dimensionality, computational
infeasibility, and strong model assumption. In this paper, we propose a novel
two-step nonparametric approach called Deep Feature Screening (DeepFS) that can
overcome these problems and identify significant features with high precision
for ultra high-dimensional, low-sample-size data. This approach first extracts
a low-dimensional representation of input data and then applies feature
screening based on multivariate rank distance correlation recently developed by
Deb and Sen (2021). This approach combines the strengths of both deep neural
networks and feature screening, and thereby has the following appealing
features in addition to its ability of handling ultra high-dimensional data
with small number of samples: (1) it is model free and distribution free; (2)
it can be used for both supervised and unsupervised feature selection; and (3)
it is capable of recovering the original input data. The superiority of DeepFS
is demonstrated via extensive simulation studies and real data analyses.

33
The categories of this paper are: plasm-ph
2
The categories of this paper are: math
37
The categories of this paper are: supr-con
35
The categories of this paper are: mtrl-th
4
The categories of this paper are: cond-mat
8
The categories of this paper are: hep-ex
14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
36
The categories of this paper are: cmp-lg
10
The categories of this paper are: q-bio
25
The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an
9
The categories of this paper are: nlin
12
The categories of this paper are: nucl-th
26
The categories of this paper are: bayes-an
23
The categories of this paper are: solv-int