具有Log型惩罚函数的稀疏正则化

A sparse regularization approach with Log type penalty

研究具有Log型惩罚函数的稀疏正则化,给出一种新的非凸变量选择及压缩感知策略,提出一种高效快速阈值迭代算法.并通过变量选择问题和稀疏信号重建验证了所提出的Log型稀疏正则化模型的有效性.

We study a sparse regularization approach with a Log type penalty function. A new strategy of nonconvex variable selection and compressive sensing is proposed with a alternative thresholding algorithm for fast solution. Then we use variable selection experiment and signal recovery experiment to prove the validity of the sparse regularization with Log type penalty.