期刊名称：International Journal of Signal Processing, Image Processing and Pattern Recognition
印刷版ISSN：2005-4254
出版年度：2016
卷号：9
期号：4
页码：361-368
DOI：10.14257/ijsip.2016.9.4.32
出版社：SERSC
摘要：This paper introduces a novel algorithm to solve the matrix rank minimization problem among all matrices obeying a set of convex constraints. The most popular convex relaxation of the rank minimization problem minimizes the nuclear norm instead of the rank of the matrix. In this paper we are interested in using robust Gaussian function to solve the low-rank matrix completion problem, which is the special case of the rank minimization problem. This regularized problem is a differential smooth convex optimization problem, in which the objective function is the sum of a convex smooth function. The numerical results suggest that our algorithm is efficient and robust in solving randomly generated matrix completion problems. Finally, we test our algorithm on low-rank image recovery problem.
