Multilayer denoising extreme learning machine

被引：0

作者：

Wang X.-D. ^{[1
]}

Lai J. ^{[1
]}

Li R. ^{[1
]}

Zhao Z.-C. ^{[1
]}

Lei L. ^{[1
]}

机构：

[1] Air and Missile Defense College, Air Force Engineering University, Xi'an

来源：

Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2020年 / 50卷 / 03期

关键词：

Artificial intelligence; Deep learning; Denoising autoencoder; Extreme learning machine; Feature extraction; Robustness;

D O I：

10.13229/j.cnki.jdxbgxb20190073

中图分类号：

学科分类号：

摘要：

In order to improve the effectiveness and robustness of the features extracted by the extreme learning machine autoencoder (ELM-AE), improve the performance of the multilayer extreme learning machine (ML-ELM) and suppress the influence of noise, an extreme learning machine denoising autoencoder (ELM-DAE) is proposed. This is based on combining the extreme learning machine (ELM) with denoising autoencoder (DAE) and introducing the corrupting process into ELM-AE, Then the multilayer denoising extreme learning machine (ML-D-ELM) is also proposed by stacking ELM-DAE. In ML-D-ELM, the highly effective and robust features are extracted by the stacked ELM-DAE, and then the classification is completed by ELM to map the abstract features to class label. Experimental results on some benchmark datasets show that ML-D-ELM can provide higher accuracy than ELM, SAE-ELM, and ML-ELM, even when considering noise. For MNIST, the accuracy of ML-D-ELM can reach 98.81%•. © 2020, Jilin University Press. All right reserved.

引用

页码：1031 / 1039

页数：8

共 27 条

[1] Huang G.B., Zhu Q.Y., Siew C.K., Extreme learning machine: theory and applications, Neurocomputing, 70, 1-3, pp. 489-501, (2006)
[2] Huang G.B., Chen L., Siew C.K., Universal approximation using incremental constructive feedforward networks with random hidden nodes, IEEE Transactions on Neural Networks, 17, 4, pp. 879-892, (2006)
[3] Huang G.B., Zhou H., Ding X., Et al., Extreme learning machine for regression and multiclass classification, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 42, 2, pp. 513-529, (2012)
[4] Liang N.Y., Huang G.B., Saratchandran P., Et al., A fast and accurate online sequential learning algorithm for feedforward networks, IEEE Transactions on Neural Networks, 17, 6, pp. 1411-1423, (2006)
[5] Zong W., Huang G.B., Chen Y., Weighted extreme learning machine for imbalance learning, Neurocomputing, 101, pp. 229-242, (2013)
[6] Deng W.Y., Zheng Q.H., Chen L., Et al., Research on extreme learning of neural networks, Chinese Journal of Computers, 33, 2, pp. 279-287, (2010)
[7] Huang H., Feng X.-A., Wei Y., Et al., An intelligent system based on enhanced kernel extreme learning machine for choosing the second major, Journal of Jilin University (Engineering and Technology Edition), 48, 4, pp. 1224-1230, (2018)
[8] Horata P., Chiewchanwattana S., Sunat K., Robust extreme learning machine, Neurocomputing, 102, pp. 31-44, (2013)
[9] Lan Y., Hu Z., Soh Y.C., Et al., An extreme learning machine approach for speaker recognition, Neural Computing and Applications, 22, 3-4, pp. 417-425, (2013)
[10] Xu Y., Dai Y., Dong Z.Y., Et al., Extreme learning machine-based predictor for real-time frequency stability assessment of electric power systems, Neural Computing and Applications, 22, 3-4, pp. 501-508, (2013)

← 1 2 3 →