Median-mean line based discriminant analysis

Abstract

This paper presents a median–mean line based discriminant analysis (MMLDA) technique for dimensionality reduction. Taking the negative effect on the class-mean caused by outliers into account, MMLDA introduces the median–mean line (MML) as an adaptive class-prototype. Based on the MML, the point-to-MML distance is designed and used as the measure metric to characterize the within-class median–mean linear scatter as well as the between-class median–mean linear scatter. Such a characterization makes MMLDA more robust than many class-mean based methods, like classical Fisher linear discriminant analysis (FLDA). In addition, the connection between MMLDA and FLDA is presented in this paper. Finally, the proposed method is evaluated using the AR face database, the Yale face database, the UCI Wine database and the ETH80 object category database. The experimental results demonstrate the effectiveness of MMLDA.

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