Least absolute deviations with L1 regularization using majorization-minimization
Project description
lad
Least absolute deviations with L1 regularization using majorization-minimization. In estimation theory terms, this is the Maximum A Posterior (MAP) estimator for a Laplacian likelihood with Laplacian prior, i.e.
The MM algorithm update turns out to be
which belongs to the class of iterative reweighted least-squares: https://en.wikipedia.org/wiki/Iteratively_reweighted_least_squares
Python Version
To install the development version, proceed as follows:
git clone https://github.com/mirca/lad.git pip install -e lad
Or install the lastest version on PyPi:
pip install lad
Installation dependencies:
- tensorflow
Test dependencies:
- numpy - tensorflow - pytest - pytest-cov
R version
To install the development version, proceed as follows:
git clone https://github.com/mirca/lad.git
Inside the R console (or your favourite terminal), type:
install.packages(“devtools”) # you haven’t installed devtools already install_github(“mirca/lad”)
The R version depends on the package CVXR which can be installed as follows:
install_github("anqif/CVXR")
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