Transient performance analysis of zero-attracting LMS

Abstract

Zero-attracting least-mean-square (ZA-LMS) algorithm has been widely used for online sparse system identification. It combines the LMS framework and ${\ell }_1$-norm regularization to promote sparsity, and relies on subgradient iterations. Despite the significant interest in ZA-LMS, few works analyzed its transient behavior. The main difficulty lies in the nonlinearity of the update rule. In this work, a detailed analysis in the mean and mean-square sense is carried out in order to examine the behavior of the algorithm. Simulation results illustrate the accuracy of the model and highlight its performance through comparisons with an existing model.

5 pages, 4 figures

References in corpus (2)

• Performance Analysis of l_0 Norm Constraint Least Mean Square Algorithm
• Steady-state performance of non-negative least-mean-square algorithm and its variants

Cited by in corpus (5)

• Diffusion Leaky Zero Attracting Least Mean Square Algorithm and Its Performance Analysis
• Combined Sparse Regularization for Nonlinear Adaptive Filters
• Affine Combination of Diffusion Strategies over Networks
• Adaptive Parameters Adjustment for Group Reweighted Zero-Attracting LMS
• Transient Performance Analysis of the ${\ell }_1$-RLS