A low-rank non-convex norm method for multiview graph clustering
Alaeddine Zahir Khalide Jbilou Ahmed Ratnani
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.