Fast and robust high-dimensional sparse representation recovery using generalized SL0
arXiv preprint arXiv:1807.05497, 2018•arxiv.org
Sparse representation can be described in high dimensions and used in many applications,
including MRI imaging and radar imaging. In some cases, methods have been proposed to
solve the high-dimensional sparse representation problem, but main solution is converting
high-dimensional problem into one-dimension. Solving the equivalent problem had very
high computational complexity. In this paper, the problem of high-dimensional sparse
representation is formulated generally based on the theory of tensors, and a method for …
including MRI imaging and radar imaging. In some cases, methods have been proposed to
solve the high-dimensional sparse representation problem, but main solution is converting
high-dimensional problem into one-dimension. Solving the equivalent problem had very
high computational complexity. In this paper, the problem of high-dimensional sparse
representation is formulated generally based on the theory of tensors, and a method for …
Sparse representation can be described in high dimensions and used in many applications, including MRI imaging and radar imaging. In some cases, methods have been proposed to solve the high-dimensional sparse representation problem, but main solution is converting high-dimensional problem into one-dimension. Solving the equivalent problem had very high computational complexity. In this paper, the problem of high-dimensional sparse representation is formulated generally based on the theory of tensors, and a method for solving it based on SL0 (Smoothed Least zero-nor) is presented. Also, the uniqueness conditions for solution of the problem are considered in the high-dimensions. At the end of the paper, some numerical experiments are performed to evaluate the efficiency of the proposed algorithm and the results are presented.
arxiv.org
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