Visualization Study of High-Dimensional Data Classification Based on PCA-SVM

Abstract

This paper aims to provide a new method of visualizing high-dimensional data classification by employing principal component analysis (PCA) and support vector machine (SVM). In this method, PCA is adopted to reduce the dimension of high-dimensional data, and then SVM is used for the data classification process. At last, the classified result is projected to two-dimension mapping. The method can visualize high-dimensional data classification, and provides the information of the data near classification boundary. Research result verifies the availability and effectiveness of the method.