A Cluster-Weighted Kernel K-Means Method for Multi-View Clustering


  • Jing Liu Shanxi Agricultural University
  • Fuyuan Cao Shanxi University
  • Xiao-Zhi Gao University of Eastern Finland
  • Liqin Yu Shanxi University
  • Jiye Liang Shanxi University




Clustering by jointly exploiting information from multiple views can yield better performance than clustering on one single view. Some existing multi-view clustering methods aim at learning a weight for each view to determine its contribution to the final solution. However, the view-weighted scheme can only indicate the overall importance of a view, which fails to recognize the importance of each inner cluster of a view. A view with higher weight cannot guarantee all clusters in this view have higher importance than them in other views. In this paper, we propose a cluster-weighted kernel k-means method for multi-view clustering. Each inner cluster of each view is assigned a weight, which is learned based on the intra-cluster similarity of the cluster compared with all its corresponding clusters in different views, to make the cluster with higher intra-cluster similarity have a higher weight among the corresponding clusters. The cluster labels are learned simultaneously with the cluster weights in an alternative updating way, by minimizing the weighted sum-of-squared errors of the kernel k-means. Compared with the view-weighted scheme, the cluster-weighted scheme enhances the interpretability for the clustering results. Experimental results on both synthetic and real data sets demonstrate the effectiveness of the proposed method.




How to Cite

Liu, J., Cao, F., Gao, X.-Z., Yu, L., & Liang, J. (2020). A Cluster-Weighted Kernel K-Means Method for Multi-View Clustering. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 4860-4867. https://doi.org/10.1609/aaai.v34i04.5922



AAAI Technical Track: Machine Learning