Open Access
ARTICLE
Extreme Learning Machine with Elastic Net Regularization
Lihua Guo*
School of Electronic and information Engineering, South China University of Technology, Guangzhou, China, 510641.
* Corresponding Author: Lihua Guo,
Intelligent Automation & Soft Computing 2020, 26(3), 421-427. https://doi.org/10.32604/iasc.2020.013918
Abstract
Compared with deep neural learning, the extreme learning machine (ELM) can
be quickly converged without iteratively tuning hidden nodes. Inspired by this
merit, an extreme learning machine with elastic net regularization (ELM-EN) is
proposed in this paper. The elastic net is a regularization method that combines
LASSO and ridge penalties. This regularization can keep a balance between
system stability and solution's sparsity. Moreover, an excellent optimization
method, i.e., accelerated proximal gradient, is used to find the minimum of the
system optimization function. Various datasets from UCI repository and two
facial expression image datasets are used to validate the efficiency of our
system. Final experimental results indicate that our ELM-EN requires less
training time than multi-layer perceptron, and can achieve higher recognition
accuracy than ELM and sparse ELM.
Keywords
Cite This Article
L. Guo, "Extreme learning machine with elastic net regularization,"
Intelligent Automation & Soft Computing, vol. 26, no.3, pp. 421–427, 2020. https://doi.org/10.32604/iasc.2020.013918
Citations