Bagging Categorization Based on Enhanced Fisher Discriminant Criterion
Qiao Shi · Journal of Taiyuan University of Science and Technology · 2009
This paper proposes an enhanced Fisher discriminant criterion based on artificial interference according to generation of Fisher discriminant criterion.It based on user-defined similarity measure of samples.The similarity measure of samples is attached to the within-class scatter matrix and the between-class scatter matrix generates from Fisher criterion.It maximizes within-class similarity and minimizes between-class similarity when Fisher criterion minimizes the distance of within-class and maximizes the distance of between-class.Then a better scatter matrix is obtained.Extensive experiments performed on both ORL,AR and AR_Gray face database verify the effectiveness of the proposed method.