Supervised dimensionality reduction technology of generalized discriminant component analysis and its kernelization forms
作者：
Highlights：
• Supervised subspace projection is a major method conducive to pattern recognition.
• Generalized discriminant component analysis is aimed at dimensionality reduction.
• Kernelization forms are proposed for nonlinear subspace projection.
• Multi-dimensional Fisher discriminant analysis are improved.
• The theoretical validity and technical advantages are comprehensively verified.
摘要
•Supervised subspace projection is a major method conducive to pattern recognition.•Generalized discriminant component analysis is aimed at dimensionality reduction.•Kernelization forms are proposed for nonlinear subspace projection.•Multi-dimensional Fisher discriminant analysis are improved.•The theoretical validity and technical advantages are comprehensively verified.
论文关键词：Dimensionality reduction,Subspace projection,Generalized discriminant component analysis,Pattern recognition
论文评审过程：Received 25 November 2020, Revised 22 September 2021, Accepted 22 November 2021, Available online 25 November 2021, Version of Record 12 December 2021.
论文官网地址：https://doi.org/10.1016/j.patcog.2021.108450
