16 citations · 37 across the 8 of their papers we have counts for
8 papers
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…
High SNR Consistent Compressive Sensing Without Signal and Noise Statistics
Sreejith Kallummil, Sheetal Kalyani
Recovering the support of sparse vectors in underdetermined linear regression models, \textit{aka}, compressive sensing is important in many signal processing applications. High SN…
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…
Residual Ratio Thresholding for Model Order Selection
Sreejith Kallummil, Sheetal Kalyani
Model order selection (MOS) in linear regression models is a widely studied problem in signal processing. Techniques based on information theoretic criteria (ITC) are algorithms of…
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…