Abstract: To tackle the problem of the curse of dimensionality caused by high dimensional data,a classification oriented criterion based dimensionality reduction method was presented．The proposed criterion aims at making each training sample and samples from the same class as close as possible in the feature space and making each training sample and samples from the different classes as distant as possible in the feature space．First,for each training sample,weighted average distance of samples from the same class and weighted average distance of samples from different classes were defined．Then,based on these two concepts,total distance of samples from the same class and total distance of samples from different classes were defined．After that,Classification Oriented Criterion (COC) was proposed,which aims at minimizing the total distance of samples from the same class and maximizing the total distance of samples from different classes．Finally,a novel dimensionality reduction method based on COC was presented．The experiments on publicly available face databases ORL and Yale demonstrate that the proposed method outperforms representative dimensionality reduction methods.

Key words: Dimensionality reduction,Total distance of samples from the same class,Total distance of samples from different classes,Classification oriented criterion,Face recognition