基于去噪自编码器的极限学习机

针对极限学习机算法(ELM)参数随机赋值降低算法鲁棒性及性能受噪声影响显著的问题,将去噪自编码器(DAE)与ELM算法相结合,提出了基于去噪自编码器的极限学习机算法(DAE-ELM)。首先,通过去噪自编码器产生ELM的输入数据、输入权值与隐含层参数;然后,以ELM求得隐含层输出权值,完成对分类器的训练。该算法一方面继承了DAE的优点,自动提取的特征更具代表性与鲁棒性,对于噪声有较强的抑制作用;另一方面克服了ELM参数赋值的随机性,增强了算法鲁棒性。实验结果表明,在不含噪声影响下DAE-ELM相较于ELM、PCA-ELM、SAA-2算法,其分类错误率在MNIST数据集中至少下降了5.6%,在Fashion MNIST数据集中至少下降了3.0%,在Rectangles数据集中至少下降了2.0%,在Convex数据集中至少下降了12.7%。

Denoising autoencoder based extreme learning machine

In order to solve the problem that parameter random assignment reduces the robustness of the algorithm and the performance is significantly affected by noise of Extreme Learning Machine (ELM), combining Denoising AutoEncoder (DAE) with ELM algorithm, a DAE based ELM (DAE-ELM) algorithm was proposed. Firstly, a denoising autoencoder was used to generate the input data, input weight and hidden layer parameters of ELM. Then, the hidden layer output was obtained through ELM to complete the training of classifier. On the one hand, the advantages of DAE were inherited by the algorithm, which means the features extracted automatically were more representative and robust and were impervious to noise. On the other hand, the randomness of parameter assignment of ELM was overcome and the robustness of the algorithm was improved. The experimental results show that, compared to ELM, Principal Component Analysis ELM (PCA-ELM), SAA-2, the classification error rate of DAE-ELM at least decreases 5.6% on MNIST, 3.0% on Fashion MINIST, 2.0% on Rectangles and 12.7% on Convex.