Dual Random Projection for Linear Support Vector Machine

Abstract

Support Vector Machine (SVM) is a popular machine learning methods in the field of data analysis. The Random Projections (RP) method can solve the problem of dimensionality reduction of high-dimensional data quickly and effectively to reduce the computational cost of the related optimization problem and is widely used in the SVM method. However, the large-scale SVM has the problem of reduced classification accuracy after dimension reduction by random projection feature. Therefore, we propose a linear kernel support vector machine based on dual-random projection (drp-LSVM) for large-scale classification problem, which combines the duality recovery theory. In this paper, we analyze the geometric properties of drp-LSVM, and prove that while dividing the geometric advantage of support vector machine based on random projection (rp-LSVM), the division of hyperplane is closer to that obtained by all data training Original classifier. The experimental results show that the drp-LSVM can reduce the classification error and improve the training precision, and the performance evaluation is closer to the classifier trained with the original data while inheriting the advantages of rp-LSVM.