| Paper ID | SPTM-17.3 |
| Paper Title |
A CONVEX PENALTY FOR BLOCK-SPARSE SIGNALS WITH UNKNOWN STRUCTURES |
| Authors |
Hiroki Kuroda, Daichi Kitahara, Akira Hirabayashi, Ritsumeikan University, Japan |
| Session | SPTM-17: Sampling, Multirate Signal Processing and Digital Signal Processing 3 |
| Location | Gather.Town |
| Session Time: | Thursday, 10 June, 15:30 - 16:15 |
| Presentation Time: | Thursday, 10 June, 15:30 - 16:15 |
| Presentation |
Poster
|
| Topic |
Signal Processing Theory and Methods: [SMDSP] Sampling, Multirate Signal Processing and Digital Signal Processing |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
We propose a novel convex penalty for block-sparse signals whose block partitions are unknown a priori. We first introduce a nonconvex penalty function, where the block partition is adjusted for the signal of interest by minimizing the mixed l2/l1 norm over all possible block partitions. Then, by exploiting a variational representation of the l2 norm, we derive the proposed penalty function as a suitable convex relaxation of the nonconvex penalty. For the resulting regularization model, we provide a proximal splitting-based algorithm which is guaranteed to converge to an optimal solution. Numerical experiments show the effectiveness of the proposed penalty. |
