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    Robust alternative minimization for matrix completion

    Lu, X. and Gong, T. and Yan, P. and Yuan, Y. and Li, Xuelong (2012) Robust alternative minimization for matrix completion. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 42 (3), pp. 939-949. ISSN 1083-4419.

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    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.


    Item Type: Article
    School: Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
    Depositing User: Sarah Hall
    Date Deposited: 07 Jun 2013 08:40
    Last Modified: 09 Aug 2023 12:33


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