Multi-view subspace clustering via simultaneously learning the representation tensor and affinity matrix
作者：
Highlights：
• A novel multi-view subspace clustering method (GLTA) is proposed.
• GLTA adopts the tensor nuclear norm to explore high-order correlation among multiple features.
• GLTA exploited the manifold regularization to preserve the view-specific geometrical structures.
• GLTA can automatically assign a optimal weight for each view.
• Extensive experiments on seven real-world datasets are given for validation.
摘要
•A novel multi-view subspace clustering method (GLTA) is proposed.•GLTA adopts the tensor nuclear norm to explore high-order correlation among multiple features.•GLTA exploited the manifold regularization to preserve the view-specific geometrical structures.•GLTA can automatically assign a optimal weight for each view.•Extensive experiments on seven real-world datasets are given for validation.
论文关键词：Multi-view subspace clustering,Low-rank tensor representation,Tensor-singular value decomposition,Adaptive weights,Local manifold
论文评审过程：Received 27 June 2019, Revised 1 March 2020, Accepted 7 May 2020, Available online 16 May 2020, Version of Record 16 May 2020.
论文官网地址：https://doi.org/10.1016/j.patcog.2020.107441
