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为了降低核极限学习机的时间复杂度，提出一种基于正规方程的L2,1正则核极限学习机。将L2,1范数引入核极限学习机的目标函数中，利用正规方程法求解L2,1正则核极限学习机的最优输出权值，从而避免模型的过拟合问题，同时提高分类性能。实验结果表明，与传统的核极限学习机相比，所提核极限学习机能够有效减少学习过程中的大量矩阵运算，具有更快的学习速度和更高的分类准确率。
Abstract：In order to reduce the time complexity of the kernel extreme learning machine, the L2,1-regularized kernel extreme learning machine based on the normal equation is proposed. The L2,1-norm is introduced into the objective function of the kernel extreme learning machine, and the optimal output weights of the L2,1-regularized kernel extreme learning machine are solved by using the normal equation, which effectively avoids the overfitting problem of the model, as well as improves the classification performance. Experiment results indicate that the proposed kernel extreme learning machine can effectively decrease a large number of matrix operations in the learning process, and has faster learning speed as well as higher classification accuracy than the conventional kernel extreme learning machine.
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基本信息:
DOI：10.13682/j.issn.2095-6533.2024.03.006
中图分类号:TP18
引用信息:
[1]吴青,魏瑶,马甜露等.基于正规方程的L_(2,1)正则核极限学习机[J].西安邮电大学学报,2024,29(03):58-64.DOI:10.13682/j.issn.2095-6533.2024.03.006.
基金信息:
国家自然科学基金项目（51875457）; 陕西省自然科学基金项目（2022JQ-636）; 陕西省重点研发计划项目（2022GY-050）