Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Obtaining error-minimizing estimates and universal entry-wise error
  bounds for low-rank matrix completion   We propose a general framework for reconstructing and denoising single
entries of incomplete and noisy entries. We describe: effective algorithms for
deciding if and entry can be reconstructed and, if so, for reconstructing and
denoising it; and a priori bounds on the error of each entry, individually. In
the noiseless case our algorithm is exact. For rank-one matrices, the new
algorithm is fast, admits a highly-parallel implementation, and produces an
error minimizing estimate that is qualitatively close to our theoretical and
the state-of-the-are Nuclear Norm and OptSpace methods.

12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
14
The categories of this paper are: math-ph
19
The categories of this paper are: eess
29
The categories of this paper are: funct-an
21
The categories of this paper are: adap-org
7
The categories of this paper are: hep-th
24
The categories of this paper are: dg-ga
36
The categories of this paper are: cmp-lg
37
The categories of this paper are: supr-con
4
The categories of this paper are: cond-mat
13
The categories of this paper are: quant-ph
25
The categories of this paper are: patt-sol
28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
6
The categories of this paper are: astro-ph