Research Area:  Machine Learning

Abstract:

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 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.

Keywords:  
Clustering algorithms
Bipartite graph
Tuning
Partitioning algorithms
Fuses
Scalability
Data models

Author(s) Name:  Dong Huang; Chang-Dong Wang; Jian-Huang Lai

Publisher name:  IEEE

DOI:  10.1109/TKDE.2023.3236698

Volume Information:   Volume: 35

Paper Link:   https://ieeexplore.ieee.org/abstract/document/10016684/algorithms?tabFilter=code#algorithms