Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Compressed Randomized UTV Decompositions for Low-Rank Approximations and
  Big Data Applications   Low-rank matrix approximations play a fundamental role in numerical linear
algebra and signal processing applications. This paper introduces a novel
rank-revealing matrix decomposition algorithm termed Compressed Randomized UTV
(CoR-UTV) decomposition along with a CoR-UTV variant aided by the power method
technique. CoR-UTV is primarily developed to compute an approximation to a
low-rank input matrix by making use of random sampling schemes. Given a large
and dense matrix of size $m\times n$ with numerical rank $k$, where $k \ll
\text{min} \{m,n\}$, CoR-UTV requires a few passes over the data, and runs in
$O(mnk)$ floating-point operations. Furthermore, CoR-UTV can exploit modern
computational platforms and, consequently, can be optimized for maximum
efficiency. CoR-UTV is simple and accurate, and outperforms reported
alternative methods in terms of efficiency and accuracy. Simulations with
synthetic data as well as real data in image reconstruction and robust
principal component analysis applications support our claims.

35
The categories of this paper are: mtrl-th
23
The categories of this paper are: solv-int
11
The categories of this paper are: hep-lat
28
The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
32
The categories of this paper are: atom-ph
13
The categories of this paper are: quant-ph
29
The categories of this paper are: funct-an
24
The categories of this paper are: dg-ga
37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
31
The categories of this paper are: ao-sci
16
The categories of this paper are: stat
2
The categories of this paper are: math
26
The categories of this paper are: bayes-an
33
The categories of this paper are: plasm-ph