Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Informed Non-convex Robust Principal Component Analysis with Features   We revisit the problem of robust principal component analysis with features
acting as prior side information. To this aim, a novel, elegant, non-convex
optimization approach is proposed to decompose a given observation matrix into
a low-rank core and the corresponding sparse residual. Rigorous theoretical
analysis of the proposed algorithm results in exact recovery guarantees with
low computational complexity. Aptly designed synthetic experiments demonstrate
that our method is the first to wholly harness the power of non-convexity over
convexity in terms of both recoverability and speed. That is, the proposed
non-convex approach is more accurate and faster compared to the best available
algorithms for the problem under study. Two real-world applications, namely
image classification and face denoising further exemplify the practical
superiority of the proposed method.

28
The categories of this paper are: alg-geom
20
The categories of this paper are: acc-phys
32
The categories of this paper are: atom-ph
2
The categories of this paper are: math
29
The categories of this paper are: funct-an
34
The categories of this paper are: chem-ph
30
The categories of this paper are: q-alg
27
The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th
4
The categories of this paper are: cond-mat
8
The categories of this paper are: hep-ex
35
The categories of this paper are: mtrl-th
18
The categories of this paper are: econ
17
The categories of this paper are: q-fin
33
The categories of this paper are: plasm-ph
37
The categories of this paper are: supr-con