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

翻译：本研究提出了一种新颖的多视角聚类技术，称为“基于共识图的低秩非凸范数多视角聚类方法”（CGMVC-NC）。多视角聚类是机器学习中一项具有挑战性的任务，因为它需要整合来自多个数据源或视角的信息以准确地对数据点进行聚类。该方法利用多视角数据张量的结构特性，引入了一种非凸张量范数来识别这些视角之间的相关性。与传统方法相比，该方法在多个基准数据集上展现出更优的聚类精度。尽管所使用的张量范数具有非凸性质，但所提出的方法仍然可以利用现有算法进行高效优化。该方法为多视角数据分析提供了一种有价值的工具，并有可能增强我们在各个领域对复杂系统的理解。未来的研究可以探索将该方法应用于其他类型的数据，并扩展到其他机器学习任务中。