Multi-view subspace learning via bidirectional sparsity

作者：

Highlights：

• By using the shared subspace’s bidirectional sparsity, we propose a novel approach which can find an effectivesubspace dimension and deal with outliers simultaneously.

• We design an effective algorithm to solve the non-convex model and give its convergence analysis.

• Compared with other multi-view subspace learning methods, the extensive experimental results on real-worlddatasets present the effectiveness of our model.

摘要

•By using the shared subspace’s bidirectional sparsity, we propose a novel approach which can find an effectivesubspace dimension and deal with outliers simultaneously.•We design an effective algorithm to solve the non-convex model and give its convergence analysis.•Compared with other multi-view subspace learning methods, the extensive experimental results on real-worlddatasets present the effectiveness of our model.

论文关键词：Multi-view clustering,Subspace learning,Bidirectional sparsity,Non-convex optimization

论文评审过程：Received 26 November 2019, Revised 17 April 2020, Accepted 30 June 2020, Available online 10 July 2020, Version of Record 31 July 2020.

论文官网地址：https://doi.org/10.1016/j.patcog.2020.107524