Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generalized SURE for optimal shrinkage of singular values in low-rank
  matrix denoising   We consider the problem of estimating a low-rank signal matrix from noisy
measurements under the assumption that the distribution of the data matrix
belongs to an exponential family. In this setting, we derive generalized
Stein's unbiased risk estimation (SURE) formulas that hold for any spectral
estimators which shrink or threshold the singular values of the data matrix.
This leads to new data-driven spectral estimators, whose optimality is
discussed using tools from random matrix theory and through numerical
experiments. Under the spiked population model and in the asymptotic setting
where the dimensions of the data matrix are let going to infinity, some
theoretical properties of our approach are compared to recent results on
asymptotically optimal shrinking rules for Gaussian noise. It also leads to new
procedures for singular values shrinkage in finite-dimensional matrix denoising
for Gamma-distributed and Poisson-distributed measurements.

20
The categories of this paper are: acc-phys
1
The categories of this paper are: cs
14
The categories of this paper are: math-ph
11
The categories of this paper are: hep-lat
5
The categories of this paper are: gr-qc
6
The categories of this paper are: astro-ph
13
The categories of this paper are: quant-ph
3
The categories of this paper are: physics
22
The categories of this paper are: chao-dyn
34
The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con
12
The categories of this paper are: nucl-th
30
The categories of this paper are: q-alg
31
The categories of this paper are: ao-sci
28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph