Original paper
Recovery of simultaneous low rank and two-way sparse coefficient matrices, a nonconvex approach
Abstract
We study the problem of recovery of matrices that are simultaneously low rank and row and/or column sparse. Such matrices appear in recent applications in cognitive neuroscience, imaging, computer vision, macroeconomics, and genetics. We propose a GDT (Gradient Descent with hard Thresholding) algorithm to efficiently recover matrices with such structure, by minimizing a bi-convex function over a nonconvex set of constraints. We show linear...
Paper Details
Title
Recovery of simultaneous low rank and two-way sparse coefficient matrices, a nonconvex approach
Published Date
Jan 1, 2020
Volume
14
Issue
1
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