# Group-sparse Matrix Recovery

arXiv: https://arxiv.org/abs/1402.5077 · HTML: https://mlsanity.com/paper/1402.5077/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2014.

## Built on by (most cited first, 1 of 1)

- [Safe Screening Rules for Group OWL Models](https://mlsanity.com/paper/2504.03152/) (2025)

## Abstract (arXiv)

We apply the OSCAR (octagonal selection and clustering algorithms for regression) in recovering group-sparse matrices (two-dimensional---2D---arrays) from compressive measurements. We propose a 2D version of OSCAR (2OSCAR) consisting of the ℓ1 norm and the pair-wise ℓ∞ norm, which is convex but non-differentiable. We show that the proximity operator of 2OSCAR can be computed based on that of OSCAR. The 2OSCAR problem can thus be efficiently solved by state-of-the-art proximal splitting algorithms. Experiments on group-sparse 2D array recovery show that 2OSCAR regularization solved by the SpaRSA algorithm is the fastest choice, while the PADMM algorithm (with debiasing) yields the most accurate results.