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.

通过引入一种新颖的名为 “Consensus Graph-Based Multi-View Clustering Method Using Low-Rank Non-Convex Norm”（CGMVC-NC）的多视图聚类技术，该研究提出了一种用于多视图聚类的新技术。该方法利用多视图数据张量的结构特征，引入非凸张量范数来确定这些视图之间的相关性，相对于传统方法，这种方法在多个基准数据集上展现出卓越的聚类准确性。尽管所使用的张量范数具有非凸性，但所提出的方法仍然可以通过现有算法进行高效优化。该方法为多视图数据分析提供了有价值的工具，并有潜力提高我们对各个领域中复杂系统的理解。进一步的研究可以探索该方法在其他类型的数据上的应用，并将其扩展到其他机器学习任务。