A New Algorithm for Reducing Dimensionality of L1-CSVM Use Augmented Lagrange Method

作     者：Mingzhu Cui Liya Fan Mingzhu Cui;Liya Fan

作者机构：School of Mathematics Science Liaocheng University Liaocheng China 

出 版 物：《Journal of Applied Mathematics and Physics》 (应用数学与应用物理（英文）)

年 卷 期：2022年第10卷第1期

页      面：21-30页

学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学] 

主　　题：Support Vector Machine Dimensionality Reduction Augmented Lagrange Method Classification 

摘      要：Principal component analysis and generalized low rank approximation of matrices are two different dimensionality reduction methods. Two different dimensionality reduction algorithms are applied to the L1-CSVM model based on augmented Lagrange method to explore the variation of running time and accuracy of the model in dimensionality reduction space. The results show that the improved algorithm can greatly reduce the running time and improve the accuracy of the algorithm.