Sparse least square support vector machines based on random entropy

Abstract

Least squares support vector machines (LSSVM) has a good performance in small data samples, but can't solve the large-scale sample problems. In this paper, large data set sparse least squares support vector machines model based on stochastic entropy is proposed, and it can be applied to large-scale data samples. Firstly, the large-scale data set is divided into several subsets. Then the entropy method is used to the sparse samples in each subset. Finally, we use sparse samples sets as training samples, and use least squares support vector machine algorithm to train. The results show that the sparse least squares support vector machine model based on entropy can effectively solve the problem of large-scale data.