Robust multi-label feature selection with dual-graph regularization

作者：

Highlights：

• We design our method based on dual-graph regularization and l2,1-norm.

• Non-negative constraint with l2,1-norm ensures row-sparse property.

• Developing an optimization scheme to solve the proposed method.

• The detailed proof of convergence for optimization scheme is presented.

• Verifying the effectiveness of our method in comparison to state-of-the-art methods.

摘要

•We design our method based on dual-graph regularization and l2,1-norm.•Non-negative constraint with l2,1-norm ensures row-sparse property.•Developing an optimization scheme to solve the proposed method.•The detailed proof of convergence for optimization scheme is presented.•Verifying the effectiveness of our method in comparison to state-of-the-art methods.

论文关键词：Feature selection,Multi-label learning,Graph regularization,Classification

论文评审过程：Received 30 November 2019, Revised 11 May 2020, Accepted 7 June 2020, Available online 9 June 2020, Version of Record 10 June 2020.

论文官网地址：https://doi.org/10.1016/j.knosys.2020.106126