A multi-label feature extraction algorithm via maximizing feature variance and feature-label dependence simultaneously

作者：

Highlights：

• We derive a least-squares formulation for MDDMp technique.

• A novel multi-label feature extraction algorithm is proposed.

• Our algorithm maximizes both feature variance and feature-label dependence.

• Experiments show that our algorithm is a competitive candidate.

摘要

•We derive a least-squares formulation for MDDMp technique.•A novel multi-label feature extraction algorithm is proposed.•Our algorithm maximizes both feature variance and feature-label dependence.•Experiments show that our algorithm is a competitive candidate.

论文关键词：Multi-label classification,Dimensionality reduction,Feature extraction,Principal component analysis,Hilbert–Schmidt independence criterion,Eigenvalue problem

论文评审过程：Received 30 June 2015, Revised 21 January 2016, Accepted 21 January 2016, Available online 1 February 2016, Version of Record 9 March 2016.

论文官网地址：https://doi.org/10.1016/j.knosys.2016.01.032