A Hybrid Regularization Semi-Supervised Extreme Learning Machine Method and Its Application
Semi-supervised extreme learning machine (SS-ELM) has been applied to many classi cation and regression assignments with high performance, in which both the labeled and unlabeled data are exploited to enhance accuracy and computation ef ciency. The Laplacian manifold regularization method has been i...
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| Định dạng: | BB |
| Ngôn ngữ: | en_US |
| Thông tin xuất bản: |
IEEE Xplore
2020
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| Chủ đề: | |
| Truy cập trực tuyến: | http://tailieuso.tlu.edu.vn/handle/DHTL/9924 |
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| Tóm tắt: | Semi-supervised extreme learning machine (SS-ELM) has been applied to many classi cation and regression assignments with high performance, in which both the labeled and unlabeled data are exploited to enhance accuracy and computation ef ciency. 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 ef ciency 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 signi cant performance in multiclass classi cation 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 classi cation of uperheating 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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