GMC: Graph-Based Multi-View Clustering
Southwest Jiaotong University · University of Illinois Chicago
Abstract
Multi-view graph-based clustering aims to provide clustering solutions to multi-view data. However, most existing methods do not give sufficient consideration to weights of different views and require an additional clustering step to produce the final clusters. They also usually optimize their objectives based on fixed graph similarity matrices of all views. In this paper, we propose a general Graph-based Multi-view Clustering (GMC) to tackle these problems. GMC takes the data graph matrices of all views and fuses them to generate a unified graph matrix. The unified graph matrix in turn improves the data graph matrix of each view, and also gives the final clusters directly. The key novelty of GMC is its…
Citation impact
- FWCI
- 44.54
- Percentile
- 100%
- References
- 72
Authors
3Topics & keywords
- Computer science
- Laplacian matrix
- Cluster analysis
- Theoretical computer science
- Graph partition
- Graph
- Adjacency matrix
- Clustering coefficient