Weighted Low-Rank Tensor Representation for Multi-View Subspace Clustering

Frontiers in Physics

Fangying Zheng¹  Shuqin Wang²  Yongyong Chen³ 

[1] Department of Mathematical Sciences, Zhejiang Sci-Tech University, Hangzhou, China;Institute of Information Science, Beijing Jiaotong University, Beijing, China;School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China;

关键词: clustering;    low-rank tensor representation;    subspace clustering;    tucker decomposition;    multi-view clustering;   

DOI  :  10.3389/fphy.2020.618224

来源: DOAJ

【 摘 要 】

Multi-view clustering has been deeply explored since the compatible and complementary information among views can be well captured. Recently, the low-rank tensor representation-based methods have effectively improved the clustering performance by exploring high-order correlations between multiple views. However, most of them often express the low-rank structure of the self-representative tensor by the sum of unfolded matrix nuclear norms, which may cause the loss of information in the tensor structure. In addition, the amount of effective information in all views is not consistent, and it is unreasonable to treat their contribution to clustering equally. To address the above issues, we propose a novel weighted low-rank tensor representation (WLRTR) method for multi-view subspace clustering, which encodes the low-rank structure of the representation tensor through Tucker decomposition and weights the core tensor to retain the main information of the views. Under the augmented Lagrangian method framework, an iterative algorithm is designed to solve the WLRTR method. Numerical studies on four real databases have proved that WLRTR is superior to eight state-of-the-art clustering methods.

【 授权许可】

Unknown