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Found 7 papers, 1 papers with code
Generalized Residual Ratio Thresholding
Simultaneous orthogonal matching pursuit (SOMP) and block OMP (BOMP) are two widely used techniques for sparse support recovery in multiple measurement vector (MMV) and block sparse (BS) models respectively.
High SNR Consistent Compressive Sensing Without Signal and Noise Statistics
The HSC results available in literature for support recovery techniques applicable to underdetermined linear regression models like least absolute shrinkage and selection operator (LASSO), orthogonal matching pursuit (OMP) etc.
Noise Statistics Oblivious GARD For Robust Regression With Sparse Outliers
Both inlier and outlier noise statistics are rarely known \textit{a priori} and this limits the efficient operation of many SRIRR algorithms.
Signal and Noise Statistics Oblivious Orthogonal Matching Pursuit
Orthogonal matching pursuit (OMP) is a widely used algorithm for recovering sparse high dimensional vectors in linear regression models.
Signal and Noise Statistics Oblivious Sparse Reconstruction using OMP/OLS
Orthogonal matching pursuit (OMP) and orthogonal least squares (OLS) are widely used for sparse signal reconstruction in under-determined linear regression problems.
Tuning Free Orthogonal Matching Pursuit
We also produce a tuning free algorithm (TF-GARD) for efficient estimation in the presence of sparse outliers by extending the operating principle of TF-OMP to GARD.
High SNR Consistent Compressive Sensing
Further, the limited literature available on the high SNR consistency of subset selection procedures (SSPs) is applicable to linear regression with full rank measurement matrices only.
