SparseStep: Approximating the Counting Norm for Sparse Regularization
Abstract
The SparseStep algorithm is presented for the estimation of a sparse parameter vector in the linear regression problem. The algorithm works by adding an approximation of the exact counting norm as a constraint on the model parameters and iteratively strengthening this approximation to arrive at a sparse solution. Theoretical analysis of the penalty function shows that the estimator yields unbiased estimates of the parameter vector. An iterative majorization algorithm is derived which has a straightforward implementation reminiscent of ridge regression. In addition, the SparseStep algorithm is compared with similar methods through a rigorous simulation study which shows it often outperforms existing methods in both model fit and prediction accuracy.
Cite
@article{arxiv.1701.06967,
title = {SparseStep: Approximating the Counting Norm for Sparse Regularization},
author = {Gerrit J. J. van den Burg and Patrick J. F. Groenen and Andreas Alfons},
journal= {arXiv preprint arXiv:1701.06967},
year = {2017}
}