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Multi-View Clustering With Learned Bipartite Graph
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DOI：10.1109/ACCESS.2021.3060135.png)
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摘要
En 中文
In this paper, we propose a late fusion multi-view clustering via a learned bipartite graph (MVC-LBG). Firstly, we obtain the base partition from each single view. Then, a consensus bipartite graph is constructed by these base partitions for final clustering results. Inspired by anchors used in spectral clustering on a bipartite graph, we introduce representative points that are updated during the iterating of MVC-LBG. Compared with early fusion multi-view clustering algorithms, MVC-LBG not only dramatically improves the clustering accuracy but also has lower computational complexity. By adopting the base partitions in a new way, the proposed algorithm achieves better performance than existing late fusion multi-view clustering algorithms. Extensive experiments on six data sets demonstrate the effects and efficiency of our algorithm. Finally, we extend MVC-LBG to large-scale datasets.
Keyword:
Clustering algorithms
Partitioning algorithms
Kernel
Optimization
Bipartite graph
Sparse matrices
Matrix decomposition
Late fusion
multi-view clustering
bipartite graph
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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