  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
multi-view clustering via ensembles (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 strategy is designed
to enable efficient multi-stage fusions. With multiple views extended to many
view groups, three levels of diversity (w.r.t. features, anchors, and
neighbors, respectively) are jointly leveraged for constructing the
view-sharing bipartite graphs in the early-stage fusion. Then, a set of
diversified base clusterings for different view groups are obtained via fast
graph partitioning, which are further formulated into a unified bipartite graph
for final clustering in the late-stage fusion. Notably, FastMICE has almost
linear time and space complexity, and is free of dataset-specific tuning.
Experiments on 22 multi-view datasets demonstrate its advantages in scalability
(for extremely large datasets), superiority (in clustering performance), and
simplicity (to be applied) over the state-of-the-art. Code available:
https://github.com/huangdonghere/FastMICE.
