Unified One-Step Multi-View Spectral Clustering

Multi-view spectral clustering, which exploits the complementary information among graphs of diverse views to obtain superior clustering results, has attracted intensive attention recently. However, most existing multi-view spectral clustering methods obtain the clustering partitions in a two-step scheme, i.e., spectral embedding and subsequent $k$ -means. This two-step scheme inevitably seeks sub-optimal clustering results due to the information loss during the two-steps processes. Besides, existing multi-view spectral clustering methods do not jointly utilize the information of graphs and embedding matrices, which also degrades final clustering results. To solve these issues, we propose a unified one-step…

• NN
  National Natural Science Foundation of China

  Award: 62076228
• NS
  Natural Science Foundation of Hubei Province

  Award: 2020CFB644
• NS
  Natural Science Foundation of Shandong Province

  Award: ZR2021LZH001
• NU
  Nanjing University

  Award: KFKT2021B24