Multiview Spectral Clustering Based on Weighted Tensor Low-Rank Constraint
Many existing multiview clustering methods fail to simultaneously exploit the high-order correlations embedded in different views and the global geometric structure of each single view,resulting in inadequate clustering performance.A Weighted Tensor Low-Rank constraint-based Multiview Spectral Clustering(WTLR-MSC)method is proposed in this study to address this limitation.First,a set of transition probability matrices are constructed from each single view.Second,a three-order tensor,which is decomposed into object and error tensors,is constructed using these matrices.The object tensor is rotated and constrained using the weighted tensor nuclear norm.Thus,the high-order correlations can be investigated efficiently.Simultaneously,the nuclear norm is applied to regularize each frontal slice of the object tensor to obtain the global geometric structure of each view.This study proposes an efficient optimization algorithm to solve the challenged mathematical optimization problem.Experiments on four datasets(BBCSport,BBC4View,COIL20,and UCI Digits)indicate that WTLR-MSC outperforms many state-of-the-art multiview methods,such as ERLRT,MCA2M,and MGL-WTNN.In terms of Accuracy(ACC),Normalized Mutual Information(NMI),F1-score,Precision,and Recall,WTLR-MSC improves by approximately 1.3,1.0,1.2,1.6,and 0.8 percentage points,demonstrating an enhanced robustness of multiview clustering.
国家科技期刊平台
NSTL
万方数据
维普
