Incomplete multi-view subspace clustering based on missing-sample recovering and structural information learning
作者：
Highlights：
• A novel method for incomplete multi-view subspace clustering is proposed.
• The missing samples recovering and structural information learning are integrated.
• The consistent and specific-view structural information are learned simultaneously.
• Schatten p-norm is applied to capture the global manifold consistent information.
• Experimental results demonstrate the effectiveness of our method.
摘要
•A novel method for incomplete multi-view subspace clustering is proposed.•The missing samples recovering and structural information learning are integrated.•The consistent and specific-view structural information are learned simultaneously.•Schatten p-norm is applied to capture the global manifold consistent information.•Experimental results demonstrate the effectiveness of our method.
论文关键词：Incomplete multi-view clustering,Missing-sample recovering,Structural information learning,Subspace learning
论文评审过程：Received 16 April 2022, Revised 25 June 2022, Accepted 10 July 2022, Available online 16 July 2022, Version of Record 21 July 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.118165
