Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Compressive hyperspectral imaging via adaptive sampling and dictionary
  learning   In this paper, we propose a new sampling strategy for hyperspectral signals
that is based on dictionary learning and singular value decomposition (SVD).
Specifically, we first learn a sparsifying dictionary from training spectral
data using dictionary learning. We then perform an SVD on the dictionary and
use the first few left singular vectors as the rows of the measurement matrix
to obtain the compressive measurements for reconstruction. The proposed method
provides significant improvement over the conventional compressive sensing
approaches. The reconstruction performance is further improved by
reconditioning the sensing matrix using matrix balancing. We also demonstrate
that the combination of dictionary learning and SVD is robust by applying them
to different datasets.

13
The categories of this paper are: quant-ph
24
The categories of this paper are: dg-ga
14
The categories of this paper are: math-ph
2
The categories of this paper are: math
20
The categories of this paper are: acc-phys
35
The categories of this paper are: mtrl-th
4
The categories of this paper are: cond-mat
17
The categories of this paper are: q-fin
11
The categories of this paper are: hep-lat
32
The categories of this paper are: atom-ph
36
The categories of this paper are: cmp-lg
34
The categories of this paper are: chem-ph
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
5
The categories of this paper are: gr-qc
8
The categories of this paper are: hep-ex