Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Mixture Gaussian Signal Estimation with L_infty Error Metric   We consider the problem of estimating an input signal from noisy measurements
in both parallel scalar Gaussian channels and linear mixing systems. The
performance of the estimation process is quantified by the $\ell_\infty$ norm
error metric. We first study the minimum mean $\ell_\infty$ error estimator in
parallel scalar Gaussian channels, and verify that, when the input is
independent and identically distributed (i.i.d.) mixture Gaussian, the Wiener
filter is asymptotically optimal with probability 1. For linear mixing systems
with i.i.d. sparse Gaussian or mixture Gaussian inputs, under the assumption
that the relaxed belief propagation (BP) algorithm matches Tanaka's fixed point
equation, applying the Wiener filter to the output of relaxed BP is also
asymptotically optimal with probability 1. However, in order to solve the
practical problem where the signal dimension is finite, we apply an estimation
algorithm that has been proposed in our previous work, and illustrate that an
$\ell_\infty$ error minimizer can be approximated by an $\ell_p$ error
minimizer provided the value of $p$ is properly chosen.

22
The categories of this paper are: chao-dyn
4
The categories of this paper are: cond-mat
33
The categories of this paper are: plasm-ph
18
The categories of this paper are: econ
32
The categories of this paper are: atom-ph
24
The categories of this paper are: dg-ga
14
The categories of this paper are: math-ph
30
The categories of this paper are: q-alg
20
The categories of this paper are: acc-phys
7
The categories of this paper are: hep-th
36
The categories of this paper are: cmp-lg
28
The categories of this paper are: alg-geom
16
The categories of this paper are: stat
10
The categories of this paper are: q-bio
8
The categories of this paper are: hep-ex
5
The categories of this paper are: gr-qc