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This study introduces a novel technique for multi-view clustering known asthe "Consensus Graph-Based Multi-View Clustering Method Using Low-RankNon-Convex Norm" (CGMVC-NC). Multi-view clustering is a challenging task inmachine learning as it requires the integration of information from multipledata sources or views to cluster data points accurately. The suggested approachmakes use of the structural characteristics of multi-view data tensors,introducing a non-convex tensor norm to identify correlations between theseviews. In contrast to conventional methods, this approach demonstrates superiorclustering accuracy across several benchmark datasets. Despite the non-convexnature of the tensor norm used, the proposed method remains amenable toefficient optimization using existing algorithms. The approach provides avaluable tool for multi-view data analysis and has the potential to enhance ourunderstanding of complex systems in various fields. Further research canexplore the application of this method to other types of data and extend it toother machine-learning tasks.

A low-rank non-convex norm method for multiview graph clustering