Alternative linear discriminant classifier

摘要

Fisher linear discriminant analysis (FLDA) finds a set of optimal discriminating vectors by maximizing Fisher criterion, i.e., the ratio of the between scatter to the within scatter. One of its major disadvantages is that the number of its discriminating vectors capable to be found is bounded from above by C-1 for C-class problem. In this paper for binary-class problem, we propose alternative FLDA to breakthrough this limitation by only replacing the original between scatter with a new scatter measure. The experimental results show that our approach give impressive recognition performances compared to both the Fisher approach and linear SVM.