Enhancing information discriminant analysis: Feature extraction with linear statistical model and information-theoretic criteria
作者：
Highlights：
• We develop a novel feature transformation method for linear dimensionality reduction.
• The statistics in the transformed subspace are learned to reduce unknown parameters.
• The transformation matrix is obtained via joint optimization of MI and likelihood.
• Our method can maximize between-class separability as well as reduce estimation errors.
• Experimental results show our method performs better than other related methods.
摘要
•We develop a novel feature transformation method for linear dimensionality reduction.•The statistics in the transformed subspace are learned to reduce unknown parameters.•The transformation matrix is obtained via joint optimization of MI and likelihood.•Our method can maximize between-class separability as well as reduce estimation errors.•Experimental results show our method performs better than other related methods.
论文关键词：Feature transformation,Information theory,Mutual information,Linear statistical model,Classification
论文评审过程：Received 10 November 2015, Revised 2 June 2016, Accepted 4 June 2016, Available online 27 June 2016, Version of Record 27 June 2016.
论文官网地址：https://doi.org/10.1016/j.patcog.2016.06.004
