Fast Multi-View Clustering Via Ensembles: Towards Scalability, Superiority, and Simplicity

Despite significant progress, there remain three limitations to the previous multi-view clustering algorithms. First, they often suffer from high computational complexity, restricting their feasibility for large-scale datasets. Second, they typically fuse multi-view information via one-stage fusion, neglecting the possibilities in multi-stage fusions. Third, dataset-specific hyperparameter-tuning is frequently required, further undermining their practicability. In light of this, we propose a fast m ulti-v i ew c lustering via e nsembles (FastMICE) approach. Particularly, the concept of random view groups is presented to capture the versatile view-wise relationships, through which the hybrid early-late fusion…

• NN
  National Natural Science Foundation of China

  Awards: U22A2095, 62276277, 61976097
• NS
  Natural Science Foundation of Guangdong Province

  Award: 2021A1515012203
• NK
  National Key Research and Development Program of China

  Award: 2021YFF1201200