A Hybrid Regularization Semi-Supervised Extreme Learning Machine Method and Its Application
A Hybrid Regularization Semi-Supervised Extreme Learning Machine Method and Its Application
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一种混合正则化半监督极限学习机方法及其应用
DOI：
10.1109/access.2019.2900267
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发表时间：
2019
期刊：
影响因子：
3.9
通讯作者：
Xie Yongfang
中科院分区：
文献类型：
--
作者：
Lei Yongxiang;Cen Lihui;Chen Xiaofang;Xie Yongfang
Semi-supervised extreme learning machine (SS-ELM) has been applied to many classification and regression assignments with high performance, in which both the labeled and unlabeled data are exploited to enhance accuracy and computation efficiency. The Laplacian manifold regularization method has been incorporated to explore the geometry of the underlying manifold structure. However, the Laplacian manifold regularization lacks the extrapolating ability and biases the solution to a real constant function. In this paper, we propose a novel algorithm, the Laplacian-Hessian regularization SS-ELM (LHRSS-ELM), to enhance the performance of conventional SS-ELM. The main advantages of LHRSS-ELM are as follows: 1) LHRSS-ELM exhibits the learning capability and computational efficiency of traditional SS-ELMs; 2) LHRSS-ELM algorithm combines both Laplacian and Hessian term to enhance the extrapolating power, accuracy, and robustness and also show significant performance in multiclass classification tasks; and 3) for the purpose of pursuing the best pair of hyperparameters to establish a comparable model, we dynamically update them from sequences. The proposed algorithm is evaluated on publicly available data sets and further applied for the state classification of superheating degree in the aluminum electrolysis process. The experimental results demonstrate that the proposed mechanism is superior to the existing state-of-the-art semi-supervised learning algorithms in the matter of accuracy and robustness.
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