Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Adaptive thresholding methods have proved to yield high SNRs and fast convergence in finding the solution to the Compressed Sensing (CS) problems. Recently, it was observed that the robustness of a class of iterative sparse recovery algorithms such as Iterative Method with Adaptive Thresholding (IMAT) has outperformed the ll-known LASSO algorithm in terms of reconstruction quality, convergence speed, and the sensitivity to the noise. ,  introduce a new method towards solving the CS problem. The logic of this method is based on iterative projections of the thresholded signal onto the null-space of the sensing matrix. The thresholding is carried out by recovering the support of the desired signal by projection on thresholding subspaces. The simulations reveal that the proposed method has the capability of yielding noticeable output SNR values with about as many samples as twice the sparsity number, while other methods fail to recover the signals when approaching the algebraic bound for the number of samples required. The computational complexity of our method is also comparable to other methods as observed in the simulations.  have also extended our Algorithm to Matrix Completion (MC) scenarios and compared its efficiency to other ll-reputed approaches for MC in the literature.
['Ehsan Asadi', 'Ashkan Esmaeili', 'Farokh Marvasti']
I am writing a research paper on a idea Iterative Null-space Projection Method with Adaptive Thresholding in Sparse Signal Recovery and Matrix Completion
[{"from": "human", "value": "I am writing a research paper on a idea Iterative Null-space Projection Method with Adaptive Thresholding in Sparse Signal Recovery and Matrix Completion"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Adaptive thresholding methods have proved to yield high SNRs and fast convergence in finding the solution to the Compressed Sensing (CS) problems. Recently, it was observed that the robustness of a class of iterative sparse recovery algorithms such as Iterative Method with Adaptive Thresholding (IMAT) has outperformed the ll-known LASSO algorithm in terms of reconstruction quality, convergence speed, and the sensitivity to the noise. ,  introduce a new method towards solving the CS problem. The logic of this method is based on iterative projections of the thresholded signal onto the null-space of the sensing matrix. The thresholding is carried out by recovering the support of the desired signal by projection on thresholding subspaces. The simulations reveal that the proposed method has the capability of yielding noticeable output SNR values with about as many samples as twice the sparsity number, while other methods fail to recover the signals when approaching the algebraic bound for the number of samples required. The computational complexity of our method is also comparable to other methods as observed in the simulations.  have also extended our Algorithm to Matrix Completion (MC) scenarios and compared its efficiency to other ll-reputed approaches for MC in the literature."}]