Multi-label feature selection with missing labels
作者：
Highlights：
• This is the first attempt to conduct feature selection for multi-label classification with missing labels.
• An embedded feature selection method is proposed with which feature selection can be conducted during the process of label recovery.
• The effective l_2,p-norm regularization is imposed on the feature selection matrix to select the most discriminative features and remove noisy ones at the same time.
• Label dependency is incorporated into the model to exploit label correlations.
摘要
•This is the first attempt to conduct feature selection for multi-label classification with missing labels.•An embedded feature selection method is proposed with which feature selection can be conducted during the process of label recovery.•The effective l_2,p-norm regularization is imposed on the feature selection matrix to select the most discriminative features and remove noisy ones at the same time.•Label dependency is incorporated into the model to exploit label correlations.
论文关键词：Feature selection,Multi-label learning,Missing labels
论文评审过程：Received 21 March 2017, Revised 26 June 2017, Accepted 24 September 2017, Available online 25 September 2017, Version of Record 9 October 2017.
论文官网地址：https://doi.org/10.1016/j.patcog.2017.09.036
