Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Ray-based classification framework for high-dimensional data   While classification of arbitrary structures in high dimensions may require
complete quantitative information, for simple geometrical structures,
low-dimensional qualitative information about the boundaries defining the
structures can suffice. Rather than using dense, multi-dimensional data, we
propose a deep neural network (DNN) classification framework that utilizes a
minimal collection of one-dimensional representations, called \emph{rays}, to
construct the "fingerprint" of the structure(s) based on substantially reduced
information. We empirically study this framework using a synthetic dataset of
double and triple quantum dot devices and apply it to the classification
problem of identifying the device state. We show that the performance of the
ray-based classifier is already on par with traditional 2D images for low
dimensional systems, while significantly cutting down the data acquisition
cost.

37
The categories of this paper are: supr-con
15
The categories of this paper are: nucl-ex
30
The categories of this paper are: q-alg
24
The categories of this paper are: dg-ga
33
The categories of this paper are: plasm-ph
27
The categories of this paper are: comp-gas
32
The categories of this paper are: atom-ph
29
The categories of this paper are: funct-an
3
The categories of this paper are: physics
0
The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph
6
The categories of this paper are: astro-ph
36
The categories of this paper are: cmp-lg
2
The categories of this paper are: math
25
The categories of this paper are: patt-sol
18
The categories of this paper are: econ