Fast Multi-view Clustering via Ensembles: Towards Scalability, Superiority, and Simplicity

• URL: http://arxiv.org/abs/2203.11572v1
• Date: Tue, 22 Mar 2022 09:51:24 GMT
• Title: Fast Multi-view Clustering via Ensembles: Towards Scalability, Superiority, and Simplicity
• Authors: Dong Huang, Chang-Dong Wang, Jian-Huang Lai
• Abstract summary: We propose a fast multi-view clustering via ensembles (FastMICE) approach. The concept of random view groups is presented to capture the versatile view-wise relationships. FastMICE has almost linear time and space complexity, and is free of dataset-specific tuning.
• Score: 63.85428043085567
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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. Remarkably, FastMICE has almost linear time and space complexity, and is free of dataset-specific tuning. Experiments on twenty 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.

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