/Matrix-Completion-Methods

Several Classic Low-Rank Matrix Completion Methods, such as SVP, SVT, Sp-lp, and TNNR-ADMM...

Primary LanguageMATLABMIT LicenseMIT

Matrix-Completion-Methods

A simple demo for low-rank matrix completion, including the following methods:

  • SVP:

Meka, Raghu and Jain, Prateek and Dhillon, Inderjit S, "Guaranteed rank minimization via singular value projection", arXiv preprint arXiv:0909.5457, 2009.

  • SVT:

Cai, Jian-Feng and Candès, Emmanuel J and Shen, Zuowei, "A singular value thresholding algorithm for matrix completion", SIAM Journal on optimization, 2010.

  • Sp-lp:

Nie, Feiping and Wang, Hua and Cai, Xiao and Huang, Heng and Ding, Chris, "Robust matrix completion via joint schatten p-norm and lp-norm minimization", 2012 IEEE 12th International Conference on Data Mining, 2012.

  • TNNR-ADMM:

Hu, Yao and Zhang, Debing and Ye, Jieping and Li, Xuelong and He, Xiaofei, "Fast and accurate matrix completion via truncated nuclear norm regularization", IEEE transactions on pattern analysis and machine intelligence, 2012.

  • Sp-lp-new:

Nie, Feiping and Wang, Hua and Huang, Heng and Ding, Chris, "Joint Schatten p-norm and lp-norm robust matrix completion for missing value recovery", Knowledge and Information Systems, 2015.

  • ...

If you want to know more about matrix completion, please refer to this paper:

  • A survey on matrix completion:

Li, Xiao Peng and Huang, Lei and So, Hing Cheung and Zhao, Bo, "A survey on matrix completion: Perspective of signal processing", arXiv preprint arXiv:1901.10885, 2019.

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