Low Complexity Training Strategy for Extreme Learning Machine Used in Embedded System

doi:10.13190/j.jbupt.2017-110

JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM ›› 2018, Vol. 41 ›› Issue (2): 9-14.doi: 10.13190/j.jbupt.2017-110

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Low Complexity Training Strategy for Extreme Learning Machine Used in Embedded System

ZHANG Ke-zhong^1,2, XU Li³, WEI Zhi-qing¹, HUANG Sai¹, FENG Zhi-yong¹

1. School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China;
2. Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing 100124, China;
3. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China

Received:2017-06-18 Online:2018-04-28 Published:2018-03-17

Abstract

Abstract: Extreme learning machine (ELM) achieves faster training speed and higher classification accuracy, compared with other widely used classifiers, such as back propagation (BP), support vector machine (SVM), spectral clustering (SC), and so forth. However, ELM suffers from some drawbacks:1) ELM utilizes the calculation of inverse matrix for training, which cannot be adopted in the embedded system; 2) the training time of ELM increases dramatically for large-scale applications. To solve these drawbacks of ELM, a new training strategy called Sequential ELM (SELM) was proposed, which avoids the calculation of inverse matrix. Therefore, SELM can be adopted in the embedded system. It is proven that SELM achieves lower complexity than other widely used algorithms. Furthermore, simulations based on practical datasets indicate that the classification accuracy of SELM is higher than traditional ELM and other widely used classifiers with shorter training time.

Key words: extreme learning machine, neural network, classification, low complexity, embedded system

CLC Number:

TP183

ZHANG Ke-zhong, XU Li, WEI Zhi-qing, HUANG Sai, FENG Zhi-yong. Low Complexity Training Strategy for Extreme Learning Machine Used in Embedded System[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2018, 41(2): 9-14.

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Low Complexity Training Strategy for Extreme Learning Machine Used in Embedded System

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