Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6957292 | Signal Processing | 2018 | 14 Pages |
Abstract
We propose a robust sparse recovery formulation in impulsive noise, where â1 norm as the metric for the residual error and a class of weakly convex functions for inducing sparsity are employed. To solve the corresponding nonconvex and nonsmooth minimization, a slack variable is introduced to guarantee the convexity of the equivalent optimization problem in each block of variables. An efficient algorithm is developed for minimizing the surrogate Lagrangian based on the alternating direction method of multipliers. Model analysis guarantees that this novel robust sparse recovery formulation guarantees to attain the global optimum. Compared with several state-of-the-art algorithms, our method attains better recovery performance in the presence of outliers.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Signal Processing
Authors
Qi Liu, Chengzhu Yang, Yuantao Gu, Hing Cheung So,