16 citations · 37 across the 8 of their papers we have counts for
4 papers · 1 filter
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…