Linear discriminant analysis (LDA) is one of the most classical linear projection techniques for feature extraction, widely used in kinds of fields. Classical LDA is contributed to finding an optimal projection subspace that can maximize the between-class scatter and minimize the average within-class scatter of each class. However, the class separation problem always exists and classical LDA can not guarantee that the within-class scatter of each class get its minimum. In this paper, we proposed the k-classifiers method, which can reduce every within-class scatter of classes respectively and alleviate the class separation problem. This method will be applied in LDA and Norm LDA and achieve significant improvement. Extensive experiments performed on MNIST data sets demonstrate the effectiveness of k-classifiers.