A new feature extraction algorithm based on fisher linear discriminant analysis

This paper introduced an improved-LDA to overcome the drawbacks existing in traditional linear discriminant analysis method. It redefined the characteristic matrix by adding a weight vector which is determined by the posterior classification rate of each feature. Therefore it can discriminate different classes of samples in the projection space more effectively than traditional methods. The numerical experiments based on UCI data sets show that this method can reduce the within-class scatter and increase the recognition accuracy rate of the support vector machine.