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

12 Jun 2016Ashkan EsmaeiliFarokh Marvasti

In this paper, we will investigate the efficacy of IMAT (Iterative Method of Adaptive Thresholding) in recovering the sparse signal (parameters) for linear models with missing data. Sparse recovery rises in compressed sensing and machine learning problems and has various applications necessitating viable reconstruction methods specifically when we work with big data... (read more)

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