Stacked graph embedded extreme learning machine algorithm

被引：0

作者：

Sun W.-T. ^{[1
]}

Ge H.-W. ^{[1
,2
]}

Yao Y. ^{[1
]}

Sun L. ^{[1
,2
]}

机构：

[1] College of Computer Science and Technology, Dalian University of Technology, Dalian

[2] Key Laboratory of Symbol Computation and Knowledge Engineering, Ministry of Education, Jilin University, Changchun

来源：

Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2019年 / 49卷 / 01期

关键词：

Artificial intelligence; Deep neural network; Extreme learning machine; Graph embedding; Stacked autoencoder;

D O I：

10.13229/j.cnki.jdxbgxb20170947

中图分类号：

学科分类号：

摘要：

Extreme Learning Machine (ELM) is characterized by least training parameters, fast training speed and strong generalization ability. It has been extensively applied to train single layer feed-forward neural networks. To exploit both local near-neighbor structure and global structure information in ELM spaces, a Graph Embedded Extreme Machine Autoencoder (GEELM-AE) is proposed. In GEELM-AE, an intrinsic graph and penalty graph for graph embedding are constructed by local Fisher discrimination analysis. Further, the framework of Stacked Graph Embedded ELM (SGE-ELM) by stacking several GEELM-AEs is proposed. Experimental results on several benchmarks indicate that the SGE-ELM obtains higher accuracy and faster training speed as compared with other algorithms. This validates that the GEELM-AE can obtain effective feature representation of the original data, and the SGE-ELM van obtain high level abstract and efficient representations. © 2019, Editorial Board of Jilin University. All right reserved.

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页码：230 / 241

页数：11

共 27 条

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