Improvements on Sequential Minimal Optimization Algorithm for Support Vector Machine Based on Semi-sparse Algorithm

Abstract

Sequential Minimal Optimization (SMO) is one of simple but fast iterative algorithm for Support Vector Machine (SVM), while there is a large amount of vector multiplication in SMO, which is still expensive and time-consuming. In this paper, we propose our Semi-sparse algorithm to enhance the vector multiplication in the SMO algorithms for large-scale sparse matrices. In the worst scenario, the traditional sparse algorithm on SMO needs O(n1+n2) times of judgments and addressing on two sparse vectors which own m and n elements respectively, while Semi-sparse algorithm can nearly finish this multiplying process within O(n2). Our experimental results on two benchmarks show that the modified SVMTorch based on our Semi-sparse algorithm can perform significantly faster than SVMTorch based on the original sparse algorithm.