Multi-View Clustering Method Based on Bipartite Graph Matrix Consistency
DOI:
https://doi.org/10.15837/ijccc.2026.1.7103Keywords:
Consistent Information, Multi-view Clustering, Bipartite Graph Matrix, SelfadaptationAbstract
To improve the performance and adaptability of multi-view clustering and address issues such as the neglect of view consistency information in graph construction, sensitivity to initial values, and the inability to adaptively learn view weights in existing algorithms, this paper proposes a Multi-View Clustering method based on Bipartite Graph Matrix Consistency (BGMC). The method learns consistency information represented by consistent anchor points across multiple views, jointly optimizes the similarity bipartite graphs of each view, and uses an alternating iterative strategy to solve for the optimal bipartite graph matrix. The model integrates view weights, a unified matrix, anchor matrices, and similarity matrices into a single optimization framework and introduces an anchor point mechanism to reduce computational complexity.Experiments on five real-world datasets including 3sources and YouTube Faces show that BGMC achieves an ACC 3-8 percent higher than the optimal method, an NMI 5-10 percent higher, and a convergence speed improved by over 20 percent.
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