续嘉,
覃华,
广西大学计算机与电子信息学院 南宁 530004
详细信息
作者简介:苏一丹：男，1962年生，教授，研究方向为自然计算、数据挖掘
续嘉：男，1998年生，硕士生，研究方向为机器学习、数据挖掘
覃华：男，1972年生，教授，研究方向为凸优化机器学习
通讯作者:覃华 xuuajia@163.com
中图分类号:TP301.6计量
文章访问数:493
HTML全文浏览量:133
PDF下载量:26
被引次数:0
出版历程
收稿日期:2020-10-16
修回日期:2021-01-31
网络出版日期:2021-03-01
刊出日期:2021-09-16
Kernel Extreme Learning Machine Based on Alternating Direction Multiplier Method of Binary Splitting Operator
Yidan SU,Jia XU,
Hua QIN,
College of Computer and Electronic Information, Guangxi University, Nanning 530004, China
摘要
摘要:凸优化形式的核极限学习机(KELM)具有较高的分类准确率，但用迭代法训练凸优化核极限学习机要较传统核极限学习机的解线性方程法花费更长时间。针对此问题，该文提出一种2元裂解算子交替方向乘子法(BSADMM-KELM)来提高凸优化核极限学习机的训练速度。首先引入2元裂解算子，将求核极限学习机最优解的过程分裂为两个中间算子的优化过程，再通过中间算子的迭代计算而得到原问题的最优解。在22个UCI数据集上所提算法的训练时间较有效集法平均快29倍，较内点法平均快4倍，分类精度亦优于传统的核极限学习机；在大规模数据集上该文算法的训练时间优于传统核极限学习机。
关键词:核极限学习机/
2次规划模型/
2元裂解算子/
交替方向乘子法
Abstract:The Kernel Extreme Learning Machine (KELM) with convex optimization form has higher classification accuracy, but it takes longer time to train kelm with iterative method than solving linear equation method of traditional kelm. To solve this problem, an Alternating Direction Multiplier Method(ADMM) of Binary Splitting (BSADMM-KELM) is proposed to improve the training speed of convex optimization kernel extreme learning machine. Firstly, the process of finding the optimal solution of the kernel extreme learning machine is split into two intermediate operators by introducing a binary splitting operator, and then the optimal solution of the original problem is obtained through the iterative calculation of the intermediate operators. On 22 UCI datasets, the training time of the proposed algorithm is 29 times faster than that of the effective set method and 4 times faster than that of the interior point method. The classification accuracy of the proposed algorithm is also better than that of the traditional kernel extreme learning machine. On large-scale datasets, the training time of the proposed algorithm is better than that of the traditional kernel extreme learning machine.
Key words:Kernel Extreme Learning Machine(KELM)/
Quadratic programming/
Binary splitting operator/
Alternating Direction Multiplier Method(ADMM)
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