GOAMP achieves error-free reconstruction of sublinearly sparse signals from linear measurements when the measurement dimension exceeds a threshold matching that of Gaussian AMP, provided the non-zero support avoids a neighborhood of the origin.
An interior-point metho d for large- scale ℓ1-regularized logistic regression,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.IT 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Generalized Orthogonal Approximate Message-Passing for Sublinear Sparsity
GOAMP achieves error-free reconstruction of sublinearly sparse signals from linear measurements when the measurement dimension exceeds a threshold matching that of Gaussian AMP, provided the non-zero support avoids a neighborhood of the origin.