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    Matrix completion by truncated nuclear norm regularization

    Zhang, D. and Hu, Y. and Ye, J. and Li, Xuelong and He, X. (2012) Matrix completion by truncated nuclear norm regularization. In: UNSPECIFIED (ed.) 2012 IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE Computer Society, pp. 2192-2199. ISBN 9781467312264.

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    Estimating missing values in visual data is a challenging problem in computer vision, which can be considered as a low rank matrix approximation problem. Most of the recent studies use the nuclear norm as a convex relaxation of the rank operator. However, by minimizing the nuclear norm, all the singular values are simultaneously minimized, and thus the rank can not be well approximated in practice. In this paper, we propose a novel matrix completion algorithm based on the Truncated Nuclear Norm Regularization (TNNR) by only minimizing the smallest N-r singular values, where N is the number of singular values and r is the rank of the matrix. In this way, the rank of the matrix can be better approximated than the nuclear norm. We further develop an efficient iterative procedure to solve the optimization problem by using the alternating direction method of multipliers and the accelerated proximal gradient line search method. Experimental results in a wide range of applications demonstrate the effectiveness of our proposed approach.


    Item Type: Book Section
    School: Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
    Depositing User: Sarah Hall
    Date Deposited: 06 Jun 2013 12:55
    Last Modified: 09 Aug 2023 12:33


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