Algebraic diversity uses matched symmetry groups to average signal estimators for variance reduction, extends to blind processing via eigenvalue methods, and defines structural capacity κ as a Rényi-2 measure of information organization.
Compressed sensing
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Log-sum regularization with adaptive smoothing for the proximal operator yields state-evolution predictions that match AMP and ADMM performance, outperforming l1 regularization in low-density or high-measurement-rate regimes.
DU-PSISTA combines linear sketching with periodic ISTA and deep unfolding to achieve linear convergence to a neighborhood of the true sparse signal at lower computational cost when the period and sketch size are chosen appropriately.
citing papers explorer
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Algebraic Diversity: Principles of a Group-Theoretic Approach to Signal Processing
Algebraic diversity uses matched symmetry groups to average signal estimators for variance reduction, extends to blind processing via eigenvalue methods, and defines structural capacity κ as a Rényi-2 measure of information organization.
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Sparse Signal Recovery using Log-Sum Regularization and Adaptive Smoothing
Log-sum regularization with adaptive smoothing for the proximal operator yields state-evolution predictions that match AMP and ADMM performance, outperforming l1 regularization in low-density or high-measurement-rate regimes.
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Computationally Efficient Sparse Signal Recovery via Linear Sketching and Deep Unfolding
DU-PSISTA combines linear sketching with periodic ISTA and deep unfolding to achieve linear convergence to a neighborhood of the true sparse signal at lower computational cost when the period and sketch size are chosen appropriately.