Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Transport-based analysis, modeling, and learning from signal and data
  distributions   Transport-based techniques for signal and data analysis have received
increased attention recently. Given their abilities to provide accurate
generative models for signal intensities and other data distributions, they
have been used in a variety of applications including content-based retrieval,
cancer detection, image super-resolution, and statistical machine learning, to
name a few, and shown to produce state of the art in several applications.
Moreover, the geometric characteristics of transport-related metrics have
inspired new kinds of algorithms for interpreting the meaning of data
distributions. Here we provide an overview of the mathematical underpinnings of
mass transport-related methods, including numerical implementation, as well as
a review, with demonstrations, of several applications.

14
The categories of this paper are: math-ph
16
The categories of this paper are: stat
15
The categories of this paper are: nucl-ex
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
29
The categories of this paper are: funct-an
22
The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg
12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph
24
The categories of this paper are: dg-ga
23
The categories of this paper are: solv-int
25
The categories of this paper are: patt-sol
32
The categories of this paper are: atom-ph
19
The categories of this paper are: eess
28
The categories of this paper are: alg-geom