Learning a compressive sensing matrix with structural constraints via maximum mean discrepancy optimization

Michael Koller, Wolfgang Utschick

Abstract: Highlights•Design of structured (example: constant modulus entries) compressive sensing matrices.•Enforcing a restricted isometry property formulated as distribution matching problem.•Distribution matching measured via maximum mean discrepancy and solved via learning.•Optimized matrix can outperform random matrices in numerical experiments.