The Twist Tensor Nuclear Norm for Video Completion

@article{Hu2017TheTT,
  title={The Twist Tensor Nuclear Norm for Video Completion},
  author={Wenrui Hu and Dacheng Tao and Wensheng Zhang and Yuan Xie and Yehui Yang},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  year={2017},
  volume={28},
  pages={2961-2973}
}
In this paper, we propose a new low-rank tensor model based on the circulant algebra, namely, twist tensor nuclear norm (t-TNN). The twist tensor denotes a three-way tensor representation to laterally store 2-D data slices in order. On one hand, t-TNN convexly relaxes the tensor multirank of the twist tensor in the Fourier domain, which allows an efficient computation using fast Fourier transform. On the other, t-TNN is equal to the nuclear norm of block circulant matricization of the twist… CONTINUE READING
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Distributed optimization and statistical learning via the alternating direction method of multipliers

  • S. Boyd, N. Parikh, E. Chu, B. Peleato, J. Eckstein
  • Found. Trends Mach. Learn., vol. 3, no. 1, p. 1…
  • 2011
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