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SparseStep: Approximating the Counting Norm for Sparse Regularization - MaRDI portal

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SparseStep: Approximating the Counting Norm for Sparse Regularization

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Publication:131647

DOI10.48550/ARXIV.1701.06967arXiv1701.06967MaRDI QIDQ131647

Author name not available (Why is that?)

Publication date: 24 January 2017

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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