1. Efficient Multi-View Clustering via Unified and Discrete Bipartite Graph Learning
- Author
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Fang, Si-Guo, Huang, Dong, Cai, Xiao-Sha, Wang, Chang-Dong, He, Chaobo, and Tang, Yong
- Abstract
Although previous graph-based multi-view clustering (MVC) algorithms have gained significant progress, most of them are still faced with three limitations. First, they often suffer from high computational complexity, which restricts their applications in large-scale scenarios. Second, they usually perform graph learning either at the single-view level or at the view–consensus level, but often neglect the possibility of the joint learning of single-view and consensus graphs. Third, many of them rely on the
$k$ $\boldsymbol {u}$ $\boldsymbol {d}$ $\boldsymbol {b}$ $\boldsymbol {g}$ $\boldsymbol {l}$ https://github.com/huangdonghere/UDBGL .- Published
- 2024
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