Comparison of Several Sparse Recovery Methods for Low Rank Matrices with Random Samples
This work addresses sparse recovery for big data applications like medical imaging, but it is incremental as it compares existing methods without introducing a new approach.
The paper compares the Iterative Method of Adaptive Thresholding (IMAT) with LASSO for sparse signal recovery in linear models with random missing data, finding that IMAT outperforms LASSO in various scenarios as supported by simulations and numerical results.
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. This paper will focus on comparing the power of IMAT in reconstruction of the desired sparse signal with LASSO. Additionally, we will assume the model has random missing information. Missing data has been recently of interest in big data and machine learning problems since they appear in many cases including but not limited to medical imaging datasets, hospital datasets, and massive MIMO. The dominance of IMAT over the well-known LASSO will be taken into account in different scenarios. Simulations and numerical results are also provided to verify the arguments.