On Unifying Multi-view Self-Representations for Clustering by Tensor Multi-rank Minimization

@article{Xie2018OnUM,
  title={On Unifying Multi-view Self-Representations for Clustering by Tensor Multi-rank Minimization},
  author={Yuan Xie and Dacheng Tao and Wensheng Zhang and Yan Liu and Lei Zhang and Yanyun Qu},
  journal={International Journal of Computer Vision},
  year={2018},
  pages={1-23}
}
In this paper, we address the multi-view subspace clustering problem. Our method utilizes the circulant algebra for tensor, which is constructed by stacking the subspace representation matrices of different views and then rotating, to capture the low rank tensor subspace so that the refinement of the view-specific subspaces can be achieved, as well as the high order correlations underlying multi-view data can be explored. By introducing a recently proposed tensor factorization, namely tensor… CONTINUE READING
6 Citations
52 References
Similar Papers

References

Publications referenced by this paper.
Showing 1-10 of 52 references

Maenpaa, “Multiresolution grayscale and rotation invariant texture classification with local binary patterns,

  • T. Ojala, M. Pietikainen
  • IEEE Trans. on Pattern Recognition and Machine…
  • 2002
Highly Influential
12 Excerpts

Similar Papers

Loading similar papers…