基于极限学习机自编码器(extreme learning machine based autoencoder，ELM-AE)和误差反向传播(back propagation，BP)算法，针对ELM提出了一种改进的特征表示方法。首先，使用ELM-AE以无监督的方式学习紧凑的特征表示，即ELM-AE输出权重;其次，利用ELM-AE输出权重来初始化BP神经网络的输入权重，然后对BP网络进行监督训练;最后，用微调的BP网络输入权重初始化ELM的输入权重参数。在MNIST数据集上的实验结果表明，采用BP算法对ELM-AE学习的参数进行约束，可以得到更紧凑且具有判别性的特征表示，有助于提高ELM的性能。
Feature representation method for extreme learning machine based on ELM-AE and BP algorithms
An improved feature representation method for extreme learning machine(ELM)was proposed based on extreme learning machine based autoencoder(ELM-AE)and error back propagation(BP)algorithms.Firstly,ELM-AE was used to learn compact feature representation i.e.ELM-AE output weights,in an unsupervised way.Secondly,ELM-AE output weights were used to initialize the input weights of the BP neural network,which was then trained by BP network in a supervised way.Finally,the input weight parameters of ELM were initialized by the input weight of the fine-tuned BP network.The experimental results on MNIST dataset show that using the BP algorithm to constrain the parameters of ELM-AE learning can result in a more compact and discriminative feature representation,which helps to improve the performance of ELM.
extreme learning machine based autoencoder(ELM-AE)error back propagationextreme learning machine
万方数据
维普
