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arXiv · 1701.06967

SparseStep: Approximating the Counting Norm for Sparse Regularization

Gerrit J. J. van den Burg·Patrick J. F. Groenen·Andreas Alfons
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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.

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Gerrit J. J. van den Burg, Patrick J. F. Groenen, Andreas Alfons. 2017-01-24. SparseStep: Approximating the Counting Norm for Sparse Regularization. https://arxiv.org/abs/1701.06967

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