%A ZHANG Qian, LI Hai-yang %T The iterative fraction thresholding algorithm in sparse information processing %0 Journal Article %D 2017 %J JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) %R 10.6040/j.issn.1671-9352.0.2017.192 %P 76-82 %V 52 %N 9 %U {http://lxbwk.njournal.sdu.edu.cn/CN/abstract/article_2913.shtml} %8 2017-09-20 %X In sparse information processing, l0 minimization is often relaxed to l1 minimization to find sparse solutions. However, l1 minimization has some deficiencies. The paper aims to find a more effective algorithm to find the sparse solutions. At first, a new shrinkage operator was constructed. Secondly, this shrinkage operator was proved to be the proximal mapping of some non-convex function. Then, a new iterative thresholding algorithm, iterative fraction thresholding algorithm(IFTA), was given by applying forward-backward splitting to the new optimization problem when l0-norm is replaced with this non-convex function. At last, the simulations indicate that the iterative fraction thresholding algorithm(IFTA)performs well in sparse signal reconstruction and high-dimensional variable selection.