Large-Scale Multi-View Spectral Clustering via Bipartite Graph

In this paper, we address the problem of large-scale multi-view spectral clustering. In many real-world applications, data can be represented in various heterogeneous features or views. Different views often provide different aspects of information that are complementary to each other. Several previous methods of clustering have demonstrated that better accuracy can be achieved using integrated information of all the views than just using each view individually. One important class of such methods is multi-view spectral clustering, which is based on graph Laplacian. However, existing methods are not applicable to large-scale problem for their high computational complexity. To this end, we propose a novel…

• NS
  National Science Foundation

  Awards: 1423056, IIS-1423056, CMMI-1434401, CNS-1405985, 1434401, CMMI-1434401, 1405985, CNS-1405985
• DO
  Division of Civil, Mechanical and Manufacturing Innovation

  Awards: CMMI-1434401, 1434401