High-order manifold regularized multi-view subspace clustering with robust affinity matrices and weighted TNN

作者：

Highlights：

• Our model captures local and global structural information of the samples.

• Data derive from linear or nonlinear subspaces can be accurately clustered.

• Robust affinity matrices and weighted tensor nuclear norm are used to handle noise.

• Experimental performance outperforms several state-of-the-art counter-parts.

摘要

•Our model captures local and global structural information of the samples.•Data derive from linear or nonlinear subspaces can be accurately clustered.•Robust affinity matrices and weighted tensor nuclear norm are used to handle noise.•Experimental performance outperforms several state-of-the-art counter-parts.

论文关键词：High-order manifold regularization,Robust affinity matrices,Multi-view subspace clustering,Weighted TNN

论文评审过程：Received 21 December 2021, Revised 9 June 2022, Accepted 21 September 2022, Available online 26 September 2022, Version of Record 4 October 2022.

论文官网地址：https://doi.org/10.1016/j.patcog.2022.109067