The NWFE-Algorithm is originally used to improve the accuracy of a classifier for the small sample data with higher dimension, this paper pointed out that the above algorithm also can be used to improve the accuracy of a classifier for the large sample data with lower dimension. Furthermore, in this paper, a novel separable transformation algorithm based on the outmost points denoted Liu-Transformation is proposed. For evaluating the performances of the SVM without any transformation, the SVM with the NWFE-Transformation and the SVM with the Liu-Transformation, a real data experiment by using 5-fold and Leave-one-out Cross-Validation accuracy is conducted. Experimental result shows that the SVM with the NWFE-Transformation is better than the SVM without any transformation, and the SVM with the proposed Liu-Transformation algorithm has the best performance.