Fast Multi-view Discrete Clustering with Anchor Graphs


  • Qianyao Qiang Xi’an Jiaotong University
  • Bin Zhang Xi'an Jiaotong University
  • Fei Wang Xi'an Jiaotong University
  • Feiping Nie Northwestern Polytechnical University


Multi-instance/Multi-view Learning


Generally, the existing graph-based multi-view clustering models consists of two steps: (1) graph construction; (2) eigen-decomposition on the graph Laplacian matrix to compute a continuous cluster assignment matrix, followed by a post-processing algorithm to get the discrete one. However, both the graph construction and eigen-decomposition are time-consuming, and the two-stage process may deviate from directly solving the primal problem. To this end, we propose Fast Multi-view Discrete Clustering (FMDC) with anchor graphs, focusing on directly solving the spectral clustering problem with a small time cost. We efficiently generate representative anchors and construct anchor graphs on different views. The discrete cluster assignment matrix is directly obtained by performing clustering on the automatically aggregated graph. FMDC has a linear computational complexity with respect to the data scale, which is a significant improvement compared to the quadratic one. Extensive experiments on benchmark datasets demonstrate its efficiency and effectiveness.




How to Cite

Qiang, Q., Zhang, B., Wang, F., & Nie, F. (2021). Fast Multi-view Discrete Clustering with Anchor Graphs. Proceedings of the AAAI Conference on Artificial Intelligence, 35(11), 9360-9367. Retrieved from



AAAI Technical Track on Machine Learning IV