Abstract
low-rank approximation problem has recently attracted wide concern due to its excellent performance in realworld applications such as image restoration, traffic monitoring, and face recognition. Compared with the classic nuclear norm, the Schatten-p norm is stated to be a closer approximation to restrain the singular values for practical applications in the real world. However, Schatten-p norm minimization is a challenging non-convex, non-smooth, and non-Lipschitz problem. In this paper, inspired by the reweighted $\ell_{1}$ norm for compressive sensing, the generalized iterative reweighted nuclear norm (GIRNN) algorithm is proposed to approximate Schatten-p norm minimization. By involving the proposed algorithms, the problem becomes more tractable and the closed solutions are derived from the iteratively reweighted subproblems. Numerical experiments for the practical matrix completion (MC) problem and robust principal component analysis (RPCA) problem are illustrated to validate the superior performance of both algorithms over some common state-of-the-art methods.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.