Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
8897658 | Linear Algebra and its Applications | 2018 | 40 Pages |
Abstract
This paper considers the problem of recovering a group sparse signal matrix Y=[y1,â¯,yL] from sparsely corrupted measurements M=[A(1)y1,â¯,A(L)yL]+S, where A(i)'s are known sensing matrices and S is an unknown sparse error matrix. A robust group lasso (RGL) model is proposed to recover Y and S through simultaneously minimizing the â2,1-norm of Y and the â1-norm of S under the measurement constraints. We prove that Y and S can be exactly recovered from the RGL model with high probability for a very general class of A(i)'s.
Related Topics
Physical Sciences and Engineering
Mathematics
Algebra and Number Theory
Authors
Xiaohan Wei, Qing Ling, Zhu Han,