Efficient and Effective Incomplete Multi-View Clustering


  • Xinwang Liu National University of Defense Technology
  • Xinzhong Zhu Zhejiang Normal University
  • Miaomiao Li Changsha University
  • Chang Tang China University of Geosciences
  • En Zhu National University of Defense Technology
  • Jianping Yin Dongguan University of Technology
  • Wen Gao Peking University




Incomplete multi-view clustering (IMVC) optimally fuses multiple pre-specified incomplete views to improve clustering performance. Among various excellent solutions, the recently proposed multiple kernel k-means with incomplete kernels (MKKM-IK) forms a benchmark, which redefines IMVC as a joint optimization problem where the clustering and kernel matrix imputation tasks are alternately performed until convergence. Though demonstrating promising performance in various applications, we observe that the manner of kernel matrix imputation in MKKM-IK would incur intensive computational and storage complexities, overcomplicated optimization and limitedly improved clustering performance. In this paper, we propose an Efficient and Effective Incomplete Multi-view Clustering (EE-IMVC) algorithm to address these issues. Instead of completing the incomplete kernel matrices, EE-IMVC proposes to impute each incomplete base matrix generated by incomplete views with a learned consensus clustering matrix. We carefully develop a three-step iterative algorithm to solve the resultant optimization problem with linear computational complexity and theoretically prove its convergence. Further, we conduct comprehensive experiments to study the proposed EE-IMVC in terms of clustering accuracy, running time, evolution of the learned consensus clustering matrix and the convergence. As indicated, our algorithm significantly and consistently outperforms some state-of-the-art algorithms with much less running time and memory.




How to Cite

Liu, X., Zhu, X., Li, M., Tang, C., Zhu, E., Yin, J., & Gao, W. (2019). Efficient and Effective Incomplete Multi-View Clustering. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 4392-4399. https://doi.org/10.1609/aaai.v33i01.33014392



AAAI Technical Track: Machine Learning