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Parallel matrix factorization for low-rank tensor completion

Yangyang Xu Ruru Hao Wotao Yin Zhixun Su

Numerical Linear Algebra mathscidoc:1912.43142

Inverse Problems and Imaging, 9, (2), 601-624, 2013
Higher-order low-rank tensors naturally arise in many applications including hyperspectral data recovery, video inpainting, seismic data recon-struction, and so on. We propose a new model to recover a low-rank tensor by simultaneously performing low-rank matrix factorizations to the all-mode ma-tricizations of the underlying tensor. An alternating minimization algorithm is applied to solve the model, along with two adaptive rank-adjusting strategies when the exact rank is not known.
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@inproceedings{yangyang2013parallel,
  title={Parallel matrix factorization for low-rank tensor completion},
  author={Yangyang Xu, Ruru Hao, Wotao Yin, and Zhixun Su},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221112525220415702},
  booktitle={Inverse Problems and Imaging},
  volume={9},
  number={2},
  pages={601-624},
  year={2013},
}
Yangyang Xu, Ruru Hao, Wotao Yin, and Zhixun Su. Parallel matrix factorization for low-rank tensor completion. 2013. Vol. 9. In Inverse Problems and Imaging. pp.601-624. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221112525220415702.
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