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arXiv · 1402.5077

Group-sparse Matrix Recovery

Xiangrong Zeng·Mário A. T. Figueiredo
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Abstract

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 $\ell_1$ norm and the pair-wise $\ell_{\infty}$ 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.

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Xiangrong Zeng, Mário A. T. Figueiredo. 2014-02-20. Group-sparse Matrix Recovery. https://arxiv.org/abs/1402.5077

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