Multi-label classification with Missing Labels using Label Correlation and Robust Structural Learning

作者：

Highlights：

• Unified framework for feature selection and classification with missing label.

• Learning missing label using label correlation and structure of data.

• Learning label specific features.

• Experimental evaluations on ten multi-label datasets proves effectiveness of model.

摘要

•Unified framework for feature selection and classification with missing label.•Learning missing label using label correlation and structure of data.•Learning label specific features.•Experimental evaluations on ten multi-label datasets proves effectiveness of model.

论文关键词：Missing labels,Multi-label learning,Laplacian matrix,Nearest neighbor,Accelerated proximal gradient

论文评审过程：Received 19 March 2021, Revised 19 July 2021, Accepted 21 July 2021, Available online 28 July 2021, Version of Record 5 August 2021.

论文官网地址：https://doi.org/10.1016/j.knosys.2021.107336