when the number of training samples is limited, feature reduction plays an important role in classification of hyperspectral images. in this paper, we propose a supervised feature extraction method based on discriminant analysis (da) which uses the first principal component (pc1) to weight the scatter matrices. the proposed method, called da-pc1, copes with the small sample size problem and has...

Partial least squares regression on functional data is applied in the context of linear discriminant analysis with binary response. The discriminant coefficient function is then used to compute scores which allow to assign a new curve to one of the two classes. The method is applied to gait data and the results are compared with those given by linear discriminant analysis and logistic regressio...

A novel model for Fisher discriminant analysis is developed in this paper. In the new model, maximal Fisher criterion values of discriminant vectors and minimal statistical correlation between feature components extracted by discriminant vectors are simultaneously required. Then the model is transformed into an extreme value problem, in the form of an evaluation function. Based on the evaluatio...