REGRESSION MODELING VIA SPARSE REGULARIZATION AND ITS ALGORITHMS

Abstract

　This paper reviews several sparse regularization methods and its algorithms. First, the regression modeling with sparse regularization is described, which includes lasso, group lasso, fused lasso, and generalized lasso. Second, we introduce two algorithms for estimating the parameters in models with sparse regularization; the coordinate descent algorithm and the alternating direction method of multipliers. Simulation results are given to investigate the properties of the two algorithms.