期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 45, 期 9, 页码 2013-2025出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2014.2363492
关键词
Deep learning; eigenvalue; extreme learning machine (ELM); feature mapping; principal component analysis (PCA); support vector machines (SVMs)
类别
资金
- Singapore Academic Research Fund (AcRF) Tier 1 [RG 22/08 (M52040128)]
- Singapore's National Research Foundation [NRF2011NRF-CRP001-090]
Extreme learning machine (ELM) has recently attracted many researchers' interest due to its very fast learning speed, good generalization ability, and ease of implementation. It provides a unified solution that can be used directly to solve regression, binary, and multiclass classification problems. In this paper, we propose a stacked ELMs (S-ELMs) that is specially designed for solving large and complex data problems. The S-ELMs divides a single large ELM network into multiple stacked small ELMs which are serially connected. The S-ELMs can approximate a very large ELM network with small memory requirement. To further improve the testing accuracy on big data problems, the ELM autoencoder can be implemented during each iteration of the S-ELMs algorithm. The simulation results show that the S-ELMs even with random hidden nodes can achieve similar testing accuracy to support vector machine (SVM) while having low memory requirements. With the help of ELM autoencoder, the S-ELMs can achieve much better testing accuracy than SVM and slightly better accuracy than deep belief network (DBN) with much faster training speed.
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