title: Robust alternative minimization for matrix completion.
abstract: Recently, much attention has been drawn to the problem of matrix completion, which arises in a number of fields, including computer vision, pattern recognition, sensor network, and recommendation systems. This paper proposes a novel algorithm, named robust alternative minimization (RAM), which is based on the constraint of low rank to complete an unknown matrix. The proposed RAM algorithm can effectively reduce the relative reconstruction error of the recovered matrix. It is numerically easier to minimize the objective function and more stable for large-scale matrix completion compared with other existing methods. It is robust and efficient for low-rank matrix completion, and the convergence of the RAM algorithm is also established. Numerical results showed that both the recovery accuracy and running time of the RAM algorithm are competitive with other reported methods. Moreover, the applications of the RAM algorithm to low-rank image recovery demonstrated that it achieves satisfactory performance.
authors.affiliation: Center for Optical Imagery Analysis and Learning, State Key Laboratory of Transient Optics and Photonics, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an, China. luxq666666@126.com
authors.forename: Xiaoqiang
authors.initials: X
authors.lastname: Lu
authors.forename: Tieliang
authors.initials: T
authors.lastname: Gong
authors.forename: Pingkun
authors.initials: P
authors.lastname: Yan
authors.forename: Yuan
authors.initials: Y
authors.lastname: Yuan
authors.forename: Xuelong
authors.initials: X
authors.lastname: Li
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003661:Decision Support Techniques; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated / Q000379:methods*