Corpus ID: 7157005

Low-Rank Tensor Learning with Discriminant Analysis for Action Classification and Image Recovery

@inproceedings{Jia2014LowRankTL,
  title={Low-Rank Tensor Learning with Discriminant Analysis for Action Classification and Image Recovery},
  author={Chengcheng Jia and G. Zhong and Y. Fu},
  booktitle={AAAI},
  year={2014}
}
Tensor completion is an important topic in the area of image processing and computer vision research, which is generally built on extraction of the intrinsic structure of the tensor data. Drawing on this fact, action classification, relying heavily on the extracted features of high-dimensional tensors, may indeed benefit from tensor completion techniques. In this paper, we propose a low-rank tensor completion method for action classification, as well as image recovery. Since there may exist… Expand
36 Citations
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  • L. Li, Fei Jiang, R. Shen
  • Mathematics, Computer Science
  • 2018 25th IEEE International Conference on Image Processing (ICIP)
  • 2018
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References

SHOWING 1-10 OF 36 REFERENCES
Low-Rank Matrix Recovery with Discriminant Regularization
Large Margin Low Rank Tensor Analysis
Tensor reduction error analysis — Applications to video compression and classification
Simultaneous Rectification and Alignment via Robust Recovery of Low-rank Tensors
Square Deal: Lower Bounds and Improved Relaxations for Tensor Recovery
Multi-target Tracking by Rank-1 Tensor Approximation
A New Convex Relaxation for Tensor Completion
Exact and Stable Recovery of Pairwise Interaction Tensors
A scalable optimization approach for fitting canonical tensor decompositions
Low-Rank Matrix and Tensor Completion via Adaptive Sampling
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