Multi-view Low-rank Sparse Subspace Clustering Algorithm Based on Three-way Decision
Multi-view subspace clustering is a method for learning a unified representation of all views from subspaces and exploring the latent clustering structure of data.As a clustering approach for processing high-dimensional data,subspace clustering has become a focal point in the field of multi-view clustering.Multi-view low-rank sparse subspace clustering method combines low-rank representation and sparse constraints.During the construction of the affinity matrix,this algorithm utilizes low-rank sparse constraints to capture both global and local structures of the data,thereby optimizing the performance of subspace clustering.The three-way decision,rooted in the rough set model,is a decision-making concept often applied in clustering algorithms to reflect the uncertainty relationship between objects and clusters during the clustering process.In this study,inspired by the idea of the three-way decision,a voting system is designed as the decision basis.The system is integrated with multi-view sparse subspace clustering to form a unified framework,resulting in a novel algorithm.Experimental results on various artificial and real-world datasets demonstrate that this algorithm can enhance the accuracy of multi-view clustering.
万方数据
维普
