Multi-label feature selection with shared common mode

作者：

Highlights：

• A novel embedded-based multi-label feature selection method is proposed.

• Our method extracts the shared common mode between features and labels.

• Our method uses Non-negative Matrix Factorization to enhance the interpretability.

• An optimization algorithm is proposed for our method.

• Numerous experiments are conducted to demonstrate the superiority of our method.

摘要

•A novel embedded-based multi-label feature selection method is proposed.•Our method extracts the shared common mode between features and labels.•Our method uses Non-negative Matrix Factorization to enhance the interpretability.•An optimization algorithm is proposed for our method.•Numerous experiments are conducted to demonstrate the superiority of our method.

论文关键词：Feature selection,Multi-label learning,Coupled matrix factorization,Non-negative matrix factorization,Classification

论文评审过程：Received 18 March 2019, Revised 7 February 2020, Accepted 25 March 2020, Available online 26 March 2020, Version of Record 6 April 2020.

论文官网地址：https://doi.org/10.1016/j.patcog.2020.107344