Group-Sparse Signal Denoising: Non-Convex Regularization, Convex Optimization Po-Yu Chen and Ivan Selesnick IEEE Transactions on Signal Processing, vol.62, no.13, pp. 3464-3478, July 1, 2014. Preprint: http://arxiv.org/abs/1308.5038 Web: http://eeweb.poly.edu/iselesni/ncogs/
Software version: 6.2
This algorithm performs group-sparse thresholding. The algorithm is intended for denoising signals that posses a group sparse structure. The approach is based on overlapping group sparsity (OGS). Although the regularizer is non-convex, it is designed such that the total cost function is convex. The comparison to convex-regularized OGS demonstrates the improvement obtained by non-convex regularization.
This research was supported by the NSF under Grant No. CCF-1018020.