A novel consensus learning approach to incomplete multi-view clustering

Highlights：

• A framework of jointly exploiting the complementary multi-view information of original data representation and the underlying cross-view relations among data points is proposed.

• The proposed method can partition unlabeled data with or without negative entries and handle complete as well as various incomplete multi-view scenarios with missing instances.

• An iterative optimization algorithm is proposed for solving the objective function.

• Extensive experiments on eight multi-view datasets demonstrate that the proposed method outperforms eight state-of-the-art methods.

摘要

•A framework of jointly exploiting the complementary multi-view information of original data representation and the underlying cross-view relations among data points is proposed.•The proposed method can partition unlabeled data with or without negative entries and handle complete as well as various incomplete multi-view scenarios with missing instances.•An iterative optimization algorithm is proposed for solving the objective function.•Extensive experiments on eight multi-view datasets demonstrate that the proposed method outperforms eight state-of-the-art methods.

论文关键词：Multi-view clustering,Incomplete multi-view clustering,Consensus representation,Consensus similarity graph

论文评审过程：Received 29 May 2020, Revised 3 February 2021, Accepted 6 February 2021, Available online 17 February 2021, Version of Record 25 February 2021.