关键词: 多视图, 子空间, 聚类, 自加权, 图学习

Abstract: Focused on the problem that multi-view data analysis was susceptible to the noise of the original data set and required additional steps to calculate the clustering results, a robust multi-view subspace (RMCGL) clustering algorithm based on consistent graph learning was proposed. Firstly, the potential robust representation of data in the subspace was learned in each view, and the similarity matrix of each view was obtained based on this representation. Then a unified similarity graph was learned based on the obtained multiple similarity matrices. Finally, by adding rank constraints to the Laplace matrix corresponding to the similarity graph, the obtained similarity graph had the optimal clustering structure, and the final clustering results could be obtained directly. The process was completed in a unified optimization framework, which could simultaneously learn potential robust representations, similarity matrices and consensus graphs. The clustering Accuracy (ACC) of the RMCGL algorithm is improved by 3.36%, 5.82%, and 5.71% on the BBC, 100leaves, and MSRC datasets, respectively, compared with the graph-based multi-view clustering algorithm(GMC). Experimental results show that the algorithm has a good clustering effect.

Key words: multi-view, subspace, clustering, self-weighted, graph learning