Extreme Learning Machine Based on Stacked Denoising Sparse Auto-Encoder

• Published in: Jisuanji gongcheng
• Main Author: ZHANG Guoling, WANG Xiaodan, LI Rui, LAI Jie, XIANG Qian
• Format: Article
• Language: English
• Published: Editorial Office of Computer Engineering 2020-09-01
• Subjects: extreme learning machine(elm)|denoising sparse auto-encoder(dsae)|sparsity|deep learning|feature extraction
• Online Access: https://www.ecice06.com/fileup/1000-3428/PDF/20200907.pdf

Extreme Learning Machine(ELM)randomly selects input weights and hidden-layer bias of network,which increases the complexity and reduces the robustness of network.To address the problem,this paper proposes an ELM algorithm based on stacked Denoising Sparse Auto-Encoder(sDSAE-ELM).By taking the advantage of sparse network of stacked Denoising Sparse Auto-Encoder(sDSAE),the deep features of target data are mined,and the input weight and hidden-layer bias are generated for ELM to obtain the hidden-layer output weight,and the training classifier is completed.Then sparsity constraints are added to optimize the network structure and improve the accuracy of algorithm classification.Experimental results show that the proposed algorithm has higher classification accuracy and stronger robustness than ELM,PCA-ELM,ELM-AE and DAE-ELM algorithms in processing of high-dimensional noisy data.