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In this paper, we will investigate the efficacy of IMAT (Iterative Method ofAdaptive Thresholding) in recovering the sparse signal (parameters) for linearmodels with missing data. Sparse recovery rises in compressed sensing andmachine learning problems and has various applications necessitating viablereconstruction methods specifically when we work with big data. This paper willfocus on comparing the power of IMAT in reconstruction of the desired sparsesignal with LASSO. Additionally, we will assume the model has random missinginformation. Missing data has been recently of interest in big data and machinelearning problems since they appear in many cases including but not limited tomedical imaging datasets, hospital datasets, and massive MIMO. The dominance ofIMAT over the well-known LASSO will be taken into account in differentscenarios. Simulations and numerical results are also provided to verify thearguments.

Comparison of Several Sparse Recovery Methods for Low Rank Matrices with Random Samples