Iterative regularization with the k-support norm provides a promising approach for achieving sparse recovery under wider conditions compared to traditional methods based on the ℓ1 norm.
The author introduces the eigenmatrix construction as a data-driven approach to address unstructured sparse recovery problems, offering a unified framework for such issues.