Least absolute deviations with L1 regularization using majorization-minimization
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
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
- numpy - tensorflow - pytest - pytest-cov
Inside the R console, type:
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