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Multi-View Clustering Method Based on Bipartite Graph Matrix Consistency
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DOI：10.15837/ijccc.2026.1.7103.png)
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Abstract
En 中文
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 percent higher than the optimal method, an NMI 5-10 percent higher, and a convergence speed improved by over 20 percent.
Keywords:
Consistent Information
Multi-view Clustering
Bipartite Graph Matrix
Self-adaptation
Journal
I
IF:
1.9
Papers:
28
Citations:
0