Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Recovering Quantized Data with Missing Information Using Bilinear
  Factorization and Augmented Lagrangian Method   In this paper, we propose a novel approach in order to recover a quantized
matrix with missing information. We propose a regularized convex cost function
composed of a log-likelihood term and a Trace norm term. The Bi-factorization
approach and the Augmented Lagrangian Method (ALM) are applied to find the
global minimizer of the cost function in order to recover the genuine data. We
provide mathematical convergence analysis for our proposed algorithm. In the
Numerical Experiments Section, we show the superiority of our method in
accuracy and also its robustness in computational complexity compared to the
state-of-the-art literature methods.

21
The categories of this paper are: adap-org
31
The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
12
The categories of this paper are: nucl-th
30
The categories of this paper are: q-alg
20
The categories of this paper are: acc-phys
0
The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph
11
The categories of this paper are: hep-lat
28
The categories of this paper are: alg-geom
25
The categories of this paper are: patt-sol
9
The categories of this paper are: nlin
33
The categories of this paper are: plasm-ph
35
The categories of this paper are: mtrl-th
4
The categories of this paper are: cond-mat