Multi-label feature selection via manifold regularization and dependence maximization

作者：

Highlights：

• Presenting a new multi-label feature selection method that efficiently combines manifold regularization and dependence maximization.

• Introducing a HSIC-based measurement to evaluate the dependence between the manifold space and label space.

• Developing an iterative optimization method to solve the objective function of our method MRDM with good convergence.

• Conducting extensive experiments on various multi-label data sets to demonstrate the superiority of the proposed method.

摘要

•Presenting a new multi-label feature selection method that efficiently combines manifold regularization and dependence maximization.•Introducing a HSIC-based measurement to evaluate the dependence between the manifold space and label space.•Developing an iterative optimization method to solve the objective function of our method MRDM with good convergence.•Conducting extensive experiments on various multi-label data sets to demonstrate the superiority of the proposed method.

论文关键词：Multi-label learning,Feature selection,Sparse regression,Manifold regularization,Dependence maximization

论文评审过程：Received 21 October 2020, Revised 8 April 2021, Accepted 30 June 2021, Available online 12 July 2021, Version of Record 17 July 2021.

论文官网地址：https://doi.org/10.1016/j.patcog.2021.108149