Learning-based Compressive Subsampling

The problem of recovering a structured signal x ∈ Cp from a set of dimensionality-reduced linear measurements b = Ax arises in a variety of applications, such as medical imaging, spectroscopy, Fourier optics, and computerized tomography. Due to computational and storage complexity or physical constraints imposed by the problem, the measurement matrix A ∈ Cn×p is often of the form A = PΩΨ for some ...