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A Unified Framework for Constructing Nonconvex Regularizations
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DOI：10.1109/LSP.2022.3140709.png)
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摘要
En 中文
Over the past decades, many individual nonconvex methods have been proposed to achieve better sparse recovery performance in various scenarios. However, how to construct a valid nonconvex regularization function remains open in practice. In this paper, we fill in this gap by presenting a unified framework for constructing the nonconvex regularization based on the probability density function. Meanwhile, a new nonconvex sparse recovery method constructed via the Weibull distribution is studied.
Keyword:
Probability density function
Weibull distribution
Sparse matrices
Null space
Indexes
Urban areas
Tuning
Nonconvex regularization
probability density function
cumulative distribution function
iteratively reweighted algorithms
期刊
IF:
9.6
论文数:
1.1W
被引数:
1.7W
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