Sparse Matrix Inversion with Scaled Lasso
Abstract
We propose a new method of learning a sparse nonnegative- definite target matrix. Our primary example of the target matrix is the inverse of a population covariance or correlation matrix. The algorithm first estimates each column of the target matrix by the scaled Lasso and then adjusts the matrix estimator to be symmetric. The penalty level of the scaled Lasso for each column is completely determined by data via convex minimization, without using cross-validation.
Cite
Text
Sun and Zhang. "Sparse Matrix Inversion with Scaled Lasso." Journal of Machine Learning Research, 2013.Markdown
[Sun and Zhang. "Sparse Matrix Inversion with Scaled Lasso." Journal of Machine Learning Research, 2013.](https://mlanthology.org/jmlr/2013/sun2013jmlr-sparse/)BibTeX
@article{sun2013jmlr-sparse,
title = {{Sparse Matrix Inversion with Scaled Lasso}},
author = {Sun, Tingni and Zhang, Cun-Hui},
journal = {Journal of Machine Learning Research},
year = {2013},
pages = {3385-3418},
volume = {14},
url = {https://mlanthology.org/jmlr/2013/sun2013jmlr-sparse/}
}