Tensor-SVD Based Graph Learning for Multi-View Subspace Clustering


  • Quanxue Gao Xidian University
  • Wei Xia Xidian University
  • Zhizhen Wan Xidian University
  • Deyan Xie Xidian University
  • Pu Zhang Xidian University




Low-rank representation based on tensor-Singular Value Decomposition (t-SVD) has achieved impressive results for multi-view subspace clustering, but it does not well deal with noise and illumination changes embedded in multi-view data. The major reason is that all the singular values have the same contribution in tensor-nuclear norm based on t-SVD, which does not make sense in the existence of noise and illumination change. To improve the robustness and clustering performance, we study the weighted tensor-nuclear norm based on t-SVD and develop an efficient algorithm to optimize the weighted tensor-nuclear norm minimization (WTNNM) problem. We further apply the WTNNM algorithm to multi-view subspace clustering by exploiting the high order correlations embedded in different views. Extensive experimental results reveal that our WTNNM method is superior to several state-of-the-art multi-view subspace clustering methods in terms of performance.




How to Cite

Gao, Q., Xia, W., Wan, Z., Xie, D., & Zhang, P. (2020). Tensor-SVD Based Graph Learning for Multi-View Subspace Clustering. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 3930-3937. https://doi.org/10.1609/aaai.v34i04.5807



AAAI Technical Track: Machine Learning