3 citations · 4 across the 3 of their papers we have counts for
6 papers · 1 filter
Generalized Residual Ratio Thresholding
Sreejith Kallummil, Sheetal Kalyani
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 spars…
Noise Statistics Oblivious GARD For Robust Regression With Sparse Outliers
Sreejith Kallummil, Sheetal Kalyani
Linear regression models contaminated by Gaussian noise (inlier) and possibly unbounded sparse outliers are common in many signal processing applications. Sparse recovery inspired…
Signal and Noise Statistics Oblivious Orthogonal Matching Pursuit
Sreejith Kallummil, Sheetal Kalyani
Orthogonal matching pursuit (OMP) is a widely used algorithm for recovering sparse high dimensional vectors in linear regression models. The optimal performance of OMP requires \te…
Signal and Noise Statistics Oblivious Sparse Reconstruction using OMP/OLS
Sreejith Kallummil, Sheetal Kalyani
Orthogonal matching pursuit (OMP) and orthogonal least squares (OLS) are widely used for sparse signal reconstruction in under-determined linear regression problems. The performanc…
Tuning Free Orthogonal Matching Pursuit
Sreejith Kallummil, Sheetal Kalyani
Orthogonal matching pursuit (OMP) is a widely used compressive sensing (CS) algorithm for recovering sparse signals in noisy linear regression models. The performance of OMP depend…
High SNR Consistent Compressive Sensing
Sreejith Kallummil, Sheetal Kalyani
High signal to noise ratio (SNR) consistency of model selection criteria in linear regression models has attracted a lot of attention recently. However, most of the existing litera…