基于三支决策的多视图低秩稀疏子空间聚类算法

Multi-view Low-rank Sparse Subspace Clustering Algorithm Based on Three-way Decision

多视图子空间聚类是一种从子空间中学习所有视图共享的统一表示,挖掘数据潜在聚类结构的方法.作为一种处理高维数据的聚类方法,子空间聚类是多视图聚类领域的研究热点之一.多视图低秩稀疏子空间聚类是一种结合了低秩表示和稀疏约束的子空间聚类方法.该算法在构造亲和矩阵过程中,利用低秩稀疏约束同时捕捉了数据的全局结构和局部结构,优化了子空间聚类的性能.三支决策是一种基于粗糙集模型的决策思想,常被应用于聚类算法来反映聚类过程中对象与类簇之间的不确定性关系.本文基于三支决策的思想,设计了一种投票制度作为决策依据,将其与多视图稀疏子空间聚类组成一个统一框架,从而形成一种新的算法.在多个人工数据集和真实数据集上的实验表明,该算法可提高多视图聚类的准确性.

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 decisionmaking 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.