WANG Youhua, ZHANG Yiming, ZHANG Jianqiu, et al., “Greedy Matrix Completion with Fitting Error and Rank Iterative Minimization,” Chinese Journal of Electronics, vol. 26, no. 4, pp. 814-819, 2017, doi: 10.1049/cje.2017.06.013
Citation: WANG Youhua, ZHANG Yiming, ZHANG Jianqiu, et al., “Greedy Matrix Completion with Fitting Error and Rank Iterative Minimization,” Chinese Journal of Electronics, vol. 26, no. 4, pp. 814-819, 2017, doi: 10.1049/cje.2017.06.013

Greedy Matrix Completion with Fitting Error and Rank Iterative Minimization

doi: 10.1049/cje.2017.06.013
Funds:  This work is supported by the National Natural Science Foundation of China (No.61171127, No.61571131).
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  • Corresponding author: ZHANG Yiming (corresponding author) was born in 1990. He is currently a global academic fellow in Statistics at New York University Abu Dhabi. His research interests include statistical machine learning and signal processing. (Email: yiming. zhang@nyu.edu)
  • Received Date: 2015-03-09
  • Rev Recd Date: 2015-07-14
  • Publish Date: 2017-07-10
  • A novel matrix completion algorithm which iteratively minimizes the fitting error and the matrix rank is presented. Unlike conventional matrix completion algorithms, which usually require some relaxation technique to cope with the low rank constraints, the proposed algorithm does not require any such techniques, thus making the selection of the parameter q of the matrix q-norm (0 < q ≤1) or the regularization parameter unnecessary. Simulation results of the random generated data and Jester joke data set verify our algorithm's effectiveness and superiority over the reported algorithms in literature.
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