Block Sparse Vector Recovery for Compressive Sensing via
$\ell_1-\alpha\ell_q$-minimization
Model
Abstract
This paper solves the problem of block sparse vector recovery using the
block
$\ell_1-\alpha\ell_q$-
minimization model. Based on the block restricted isometry property
(B-RIP) condition, we obtain exact block sparse vector recovery result.
We also obtain the theoretical bound for the block
$\ell_1-\alpha\ell_q$-
minimization model when measurements are depraved by the noises.