常州大学学报（自然科学版）2026，Vol.38Issue(3)：72-81,10.DOI:10.3969/j.issn.2095-0411.2026.03.009
复杂网络上的加速核学习算法
Accelerated kernel learning algorithm over complex networks
摘要
Abstract
In this paper,the kernel learning problem over complex networks is studied,and the non-linear coupling functions between nodes were learned by using input-output data pairs.A kernel least mean square algorithm was designed,using the Random Fourier Feature method to reduce the compu-tational and storage complexity.To address the problem of gradient noise caused by the stochastic gradient descent algorithm,the kernel reproducing gradient descent algorithm was used to solve the mean square error function,which reduces the variance of gradient noise.Moreover,the adaptive mo-mentum strategy was adopted to accelerate the convergence of the kernel least mean square algorithm.Theoretical analyses are provided to show that the accelerated kernel learning algorithm is convergent if the learning rate meets certain conditions.Simulation examples verify the superiority of the proposed algorithm in terms of convergence speed and time cost.关键词
复杂网络/核学习/随机傅里叶特征/核再生梯度下降/自适应动量Key words
complex network/kernel learning/Random Fourier Feature/kernel reproducing gradient descent/adaptive momentum分类
信息技术与安全科学引用本文复制引用
李朵,林一夫,李文玲..复杂网络上的加速核学习算法[J].常州大学学报（自然科学版）,2026,38(3):72-81,10.基金项目
国家自然科学基金资助项目(61976013,U22B2038). （61976013,U22B2038）
