Robust generalised quadratic discriminant analysis

作者：

Highlights：

• Investigation of robustness of a Generalized Quadratic Discriminant Analysis (GQDA) under the presence of Noise in data.

• The GQDA is a novel approach integrating linear & quadratic discriminant analyses, but is extremely sensitive under mild contamination.

• Development of roust versions of GQDA classier by using robust estimators of the mean vector and the dispersion matrix.

• Detailed empirical comparison of robust GQDA proposals with 6 robust estimators, 3 classes of model distribution and 4 real data examples.

摘要

•Investigation of robustness of a Generalized Quadratic Discriminant Analysis (GQDA) under the presence of Noise in data.•The GQDA is a novel approach integrating linear & quadratic discriminant analyses, but is extremely sensitive under mild contamination.•Development of roust versions of GQDA classier by using robust estimators of the mean vector and the dispersion matrix.•Detailed empirical comparison of robust GQDA proposals with 6 robust estimators, 3 classes of model distribution and 4 real data examples.

论文关键词：Linear discriminant analysis,Quadratic discriminant analysis,Generalized quadratic discriminant analysis,Robust estimators

论文评审过程：Received 8 April 2020, Revised 22 February 2021, Accepted 31 March 2021, Available online 20 April 2021, Version of Record 4 May 2021.

论文官网地址：https://doi.org/10.1016/j.patcog.2021.107981