Arbitrary error metrics - software
This webpage describes the
used in our work on signal estimation with arbitrary error metrics, described in the following paper,
and D. Baron,
"Optimal Estimation with Arbitrary Error Metrics in Compressed Sensing,"
to appear in IEEE Statistical Signal Processing workshop,
Ann Arbor, MI, August 2012
The software was implemented by
based on an earlier implementation by
for estimation with relaxed belief propagation
(Matlab). The implementation uses Bayesian method to minimize the expected error metric of interest.
Below is a brief description of files used in our implementation. Comments will be appreciated.
Jin Tan, July 2012
main_MetricOpt.m: script that runs the relaxed belief propagation algorithm and our metric-optimal algorithm.
Px_Gen.m: function that generates the prior distribution p(x).
Pxr_Gen.m: function that generates the conditional distribution p(x|r), where r is the output of the scalar Gaussian channel by decoupling principle.
Dist_Gen.m: function that generates the distortion matrix, used to implement expected error metric integration numerically.
main (folder): the relaxed belief propagation algorithm implementation by
Note that this folder must be in the Matlab path before running main_MetricOpt.m.
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