Multi-Label Low-dimensional Embedding with Missing Labels
作者：
Highlights：
• A novel multi-label method is developed to handle label recovery to enhance classifying performance.
• The proposed method exploits the low rank property to complete a label imputation process.
• An inductive classifier is trained using the underling connections between features and labels.
• The proposed method is thoroughly tested by 12 benchmark multi-label datasets.
摘要
•A novel multi-label method is developed to handle label recovery to enhance classifying performance.•The proposed method exploits the low rank property to complete a label imputation process.•An inductive classifier is trained using the underling connections between features and labels.•The proposed method is thoroughly tested by 12 benchmark multi-label datasets.
论文关键词：Label imputation,Low rank,Instance-wise label correlation,Inductive classifier
论文评审过程：Received 8 March 2017, Revised 4 September 2017, Accepted 6 September 2017, Available online 8 September 2017, Version of Record 18 October 2017.
论文官网地址：https://doi.org/10.1016/j.knosys.2017.09.005
