基于径向基神经网络和正则化极限学习机的多标签学习模型

摘要

相比径向基(RBF)神经网络,极限学习机(ELM)训练速度更快,泛化能力更强.同时,近邻传播聚类算法(AP)可以自动确定聚类个数.因此,文中提出融合AP聚类、多标签RBF(ML-RBF)和正则化ELM(RELM)的多标签学习模型(ML-AP-RBF-RELM).首先,在该模型中输入层使用ML-RBF进行映射,且通过AP聚类算法自动确定每一类标签的聚类个数,计算隐层节点个数.然后,利用每类标签的聚类个数通过K均值聚类确定隐层节点RBF函数的中心.最后,通过RELM快速求解隐层到输出层的连接权值.实验表明,ML-AP-RBF-RELM效果较好.%Extreme learning machine ( ELM) possesses the characteristics of fast training and good generalization ability compared with radial basis function neural network(RBFNN), and the affinity propagation(AP) clustering algorithm can automatically determine the number of clusters without a prior knowledge. Therefore, a multi-label learning model named ML-AP-RBF-RELM is proposed, and AP clustering algorithm, multi-label back propagation neural network ( ML-RBF) and regularized ELM ( RELM) are integrated in this model. ML-RBF is used to map in the input layer. In the hidden layer, the number of hidden nodes can be automatically determined by the sum of clustering centers of AP algorithm, and the center of the RBF function can be computed through the center of K-means clustering algorithm while the clustering number is determined by AP algorithm. Finally, the weights from hidden layer to output layer are rapidly calculated through RELM. The simulation results demonstrate that ML-AP-RBF-RELM performs well.