An attention-based framework for multi-view clustering on Grassmann manifold

作者：

Highlights：

• This work can effectively mine the manifold structures of subspaces.

• This work can adaptively capture the differences among views.

• This work can generate clustering results without randomness.

• This work is the first multi-view clustering framework built on Grassmann manifold.

• This work is extensible and can generate many models.

摘要

•This work can effectively mine the manifold structures of subspaces.•This work can adaptively capture the differences among views.•This work can generate clustering results without randomness.•This work is the first multi-view clustering framework built on Grassmann manifold.•This work is extensible and can generate many models.

论文关键词：Multi-view clustering,Grassmann manifold,Principle angles,Attentive weighted-learning scheme

论文评审过程：Received 23 November 2020, Revised 29 November 2021, Accepted 24 February 2022, Available online 25 February 2022, Version of Record 11 April 2022.

论文官网地址：https://doi.org/10.1016/j.patcog.2022.108610