Regularized generalized eigen-decomposition with applications to sparse supervised feature extraction and sparse discriminant analysis
作者：
Highlights：
• We propose a new technique called Regularized Generalized Eigen Decomposition (RGED).
• RGED solves generalized eigenvalue problems and obtains sparse solutions.
• It is easy and straightforward applying RGED to sparse discriminant analysis and feature extraction.
• An algorithm is developed to solve it with monotonically decreasing convergence.
• RGED has competitive classification performance comparing with other methods.
摘要
Highlights•We propose a new technique called Regularized Generalized Eigen Decomposition (RGED).•RGED solves generalized eigenvalue problems and obtains sparse solutions.•It is easy and straightforward applying RGED to sparse discriminant analysis and feature extraction.•An algorithm is developed to solve it with monotonically decreasing convergence.•RGED has competitive classification performance comparing with other methods.
论文关键词：Sparse discriminant analysis,Sparse supervised feature extraction,Sparse 2D-LDA,Sparse 3D-LDA,Regularized generalized eigen-decomposition
论文评审过程：Received 9 March 2015, Revised 21 June 2015, Accepted 22 July 2015, Available online 1 August 2015, Version of Record 28 September 2015.
论文官网地址：https://doi.org/10.1016/j.patcog.2015.07.008
