Jointly smoothed multi-view subspace clustering based on feature concatenation
In recent years,the multi-view clustering problem has received widespread attention both domestically and internationally.The jointly smoothed multi-view clustering algorithm utilizes the view-consensus grouping effect and the local structure of multiple views to standardize the common representation of views,achieving impressive clustering results.However,this algorithm still has certain limitations in exploring inconsistency,which limits further improvement of the clustering performance.In order to further explore the inconsistency of multiple views,this paper proposes a jointly smoothed multi-view subspace clustering algorithm based on feature concatenation.It not only learns the consistency and inconsistency between views simultaneously to enhance view diversity,but also divides the whole inconsistency into cluster-specific and sample-specific corruptions.It is further associated with low-rank representations through kernel norm,and on such a basis of iterates using alternating direction minimization.Experiments conducted on four benchmark datasets have demonstrated the superiority of the proposed algorithm over other excellent algorithms.
万方数据
维普
