Graph Learning for Multiview Clustering

Most existing graph-based clustering methods need a predefined graph and their clustering performance highly depends on the quality of the graph. Aiming to improve the multiview clustering performance, a graph learning-based method is proposed to improve the quality of the graph. Initial graphs are learned from data points of different views, and the initial graphs are further optimized with a rank constraint on the Laplacian matrix. Then, these optimized graphs are integrated into a global graph with a well-designed optimization procedure. The global graph is learned by the optimization procedure with the same rank constraint on its Laplacian matrix. Because of the rank constraint, the cluster indicators are…

• NN
  National Natural Science Foundation of China

  Award: 61201422
• FR
  Fundamental Research Funds for the Central Universities

  Award: lzujbky-2017-190
• SR
  Specialized Research Fund for the Doctoral Program of Higher Education of China

  Award: 20120211120013