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A0449
Title: SparseStep: Approximating the counting norm for sparse regularization Authors:  Gertjan van den Burg - Erasmus University Rotterdam (Netherlands) [presenting]
Andreas Alfons - Erasmus University Rotterdam (Netherlands)
Patrick Groenen - Erasmus University Rotterdam (Netherlands)
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.