Orthogonally weighted $\ell_{2,1}$ regularization for rank-aware joint sparse recovery: algorithm and analysis

1 code implementation • 21 Nov 2023 • Armenak Petrosyan, Konstantin Pieper, Hoang Tran

We propose and analyze an efficient algorithm for solving the joint sparse recovery problem using a new regularization-based method, named orthogonally weighted $\ell_{2, 1}$ ($\mathit{ow}\ell_{2, 1}$), which is specifically designed to take into account the rank of the solution matrix.

Dictionary Learning

Nonconvex regularization for sparse neural networks

1 code implementation • 24 Apr 2020 • Konstantin Pieper, Armenak Petrosyan

Convex $\ell_1$ regularization using an infinite dictionary of neurons has been suggested for constructing neural networks with desired approximation guarantees, but can be affected by an arbitrary amount of over-parametrization.