Robust Self-Weighted Multi-View Projection Clustering


  • Beilei Wang Northwest University
  • Yun Xiao Northwest University
  • Zhihui Li University of New South Wales
  • Xuanhong Wang Xi’an University of Posts & Telecommunications
  • Xiaojiang Chen NorthWest University
  • Dingyi Fang NorthWest University



Many real-world applications involve data collected from different views and with high data dimensionality. Furthermore, multi-view data always has unavoidable noise. Clustering on this kind of high-dimensional and noisy multi-view data remains a challenge due to the curse of dimensionality and ineffective de-noising and integration of multiple views. Aiming at this problem, in this paper, we propose a Robust Self-weighted Multi-view Projection Clustering (RSwMPC) based on ℓ2,1-norm, which can simultaneously reduce dimensionality, suppress noise and learn local structure graph. Then the obtained optimal graph can be directly used for clustering while no further processing is required. In addition, a new method is introduced to automatically learn the optimal weight of each view with no need to generate additional parameters to adjust the weight. Extensive experimental results on different synthetic datasets and real-world datasets demonstrate that the proposed algorithm outperforms other state-of-the-art methods on clustering performance and robustness.




How to Cite

Wang, B., Xiao, Y., Li, Z., Wang, X., Chen, X., & Fang, D. (2020). Robust Self-Weighted Multi-View Projection Clustering. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 6110-6117.



AAAI Technical Track: Machine Learning