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Least absolute deviations with L1 regularization using majorization-minimization

Project description

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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.

lad.png

The MM algorithm update turns out to be

lad2.png

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

Inside the R console, type:

devtools::install_github("mirca/lad/r/lad")

Project details


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