Multilayer Fisher extreme learning machine for classification
As a special deep learning algorithm, the multilayer extreme learning machine (ML-ELM) has been extensively studied to solve practical problems in recent years. The ML-ELM is constructed from the extreme learning machine autoencoder (ELM-AE), and its generalization performance is affected by the rep...
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| Vydáno v: | Complex & intelligent systems Ročník 9; číslo 2; s. 1975 - 1993 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Cham
Springer International Publishing
01.04.2023
Springer Nature B.V Springer |
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| ISSN: | 2199-4536, 2198-6053 |
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| Abstract | As a special deep learning algorithm, the multilayer extreme learning machine (ML-ELM) has been extensively studied to solve practical problems in recent years. The ML-ELM is constructed from the extreme learning machine autoencoder (ELM-AE), and its generalization performance is affected by the representation learning of the ELM-AE. However, given label information, the unsupervised learning of the ELM-AE is difficult to build the discriminative feature space for classification tasks. To address this problem, a novel Fisher extreme learning machine autoencoder (FELM-AE) is proposed and is used as the component for the multilayer Fisher extreme leaning machine (ML-FELM). The FELM-AE introduces the Fisher criterion into the ELM-AE by adding the Fisher regularization term to the objective function, aiming to maximize the between-class distance and minimize the within-class distance of abstract feature. Different from the ELM-AE, the FELM-AE requires class labels to calculate the Fisher regularization loss, so that the learned abstract feature contains sufficient category information to complete classification tasks. The ML-FELM stacks the FELM-AE to extract feature and adopts the extreme leaning machine (ELM) to classify samples. Experiments on benchmark datasets show that the abstract feature extracted by the FELM-AE is more discriminative than the ELM-AE, and the classification results of the ML-FELM are more competitive and robust in comparison with the ELM, one-dimensional convolutional neural network (1D-CNN), ML-ELM, denoising multilayer extreme learning machine (D-ML-ELM), multilayer generalized extreme learning machine (ML-GELM), and hierarchical extreme learning machine with L21‑norm loss and regularization (H-LR21-ELM). |
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| AbstractList | As a special deep learning algorithm, the multilayer extreme learning machine (ML-ELM) has been extensively studied to solve practical problems in recent years. The ML-ELM is constructed from the extreme learning machine autoencoder (ELM-AE), and its generalization performance is affected by the representation learning of the ELM-AE. However, given label information, the unsupervised learning of the ELM-AE is difficult to build the discriminative feature space for classification tasks. To address this problem, a novel Fisher extreme learning machine autoencoder (FELM-AE) is proposed and is used as the component for the multilayer Fisher extreme leaning machine (ML-FELM). The FELM-AE introduces the Fisher criterion into the ELM-AE by adding the Fisher regularization term to the objective function, aiming to maximize the between-class distance and minimize the within-class distance of abstract feature. Different from the ELM-AE, the FELM-AE requires class labels to calculate the Fisher regularization loss, so that the learned abstract feature contains sufficient category information to complete classification tasks. The ML-FELM stacks the FELM-AE to extract feature and adopts the extreme leaning machine (ELM) to classify samples. Experiments on benchmark datasets show that the abstract feature extracted by the FELM-AE is more discriminative than the ELM-AE, and the classification results of the ML-FELM are more competitive and robust in comparison with the ELM, one-dimensional convolutional neural network (1D-CNN), ML-ELM, denoising multilayer extreme learning machine (D-ML-ELM), multilayer generalized extreme learning machine (ML-GELM), and hierarchical extreme learning machine with L21‑norm loss and regularization (H-LR21-ELM). Abstract As a special deep learning algorithm, the multilayer extreme learning machine (ML-ELM) has been extensively studied to solve practical problems in recent years. The ML-ELM is constructed from the extreme learning machine autoencoder (ELM-AE), and its generalization performance is affected by the representation learning of the ELM-AE. However, given label information, the unsupervised learning of the ELM-AE is difficult to build the discriminative feature space for classification tasks. To address this problem, a novel Fisher extreme learning machine autoencoder (FELM-AE) is proposed and is used as the component for the multilayer Fisher extreme leaning machine (ML-FELM). The FELM-AE introduces the Fisher criterion into the ELM-AE by adding the Fisher regularization term to the objective function, aiming to maximize the between-class distance and minimize the within-class distance of abstract feature. Different from the ELM-AE, the FELM-AE requires class labels to calculate the Fisher regularization loss, so that the learned abstract feature contains sufficient category information to complete classification tasks. The ML-FELM stacks the FELM-AE to extract feature and adopts the extreme leaning machine (ELM) to classify samples. Experiments on benchmark datasets show that the abstract feature extracted by the FELM-AE is more discriminative than the ELM-AE, and the classification results of the ML-FELM are more competitive and robust in comparison with the ELM, one-dimensional convolutional neural network (1D-CNN), ML-ELM, denoising multilayer extreme learning machine (D-ML-ELM), multilayer generalized extreme learning machine (ML-GELM), and hierarchical extreme learning machine with L21‑norm loss and regularization (H-LR21-ELM). |
| Author | Lai, Jie Wang, Jian Lei, Lei Xiang, Qian Wang, Xiaodan |
| Author_xml | – sequence: 1 givenname: Jie surname: Lai fullname: Lai, Jie organization: College of Air and Missile Defense, Air Force Engineering University – sequence: 2 givenname: Xiaodan orcidid: 0000-0003-2785-9539 surname: Wang fullname: Wang, Xiaodan email: afeu_wang@163.com organization: College of Air and Missile Defense, Air Force Engineering University – sequence: 3 givenname: Qian surname: Xiang fullname: Xiang, Qian organization: College of Air and Missile Defense, Air Force Engineering University – sequence: 4 givenname: Jian surname: Wang fullname: Wang, Jian organization: College of Air and Missile Defense, Air Force Engineering University – sequence: 5 givenname: Lei surname: Lei fullname: Lei, Lei organization: College of Information and Navigation, Air Force Engineering University |
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| Cites_doi | 10.3390/diagnostics11020241 10.1016/j.jmsy.2020.05.005 10.1109/TNNLS.2016.2636834 10.1109/TSMCB.2011.2168604 10.1007/s13042-016-0544-9 10.1177/0144598720903797 10.1007/s11045-016-0437-9 10.1016/j.neucom.2016.12.027 10.1049/iet-gtd.2019.0531 10.1109/JSEN.2020.3019777 10.3390/app10124125 10.1109/ACCESS.2019.2921390 10.1007/s13042-019-00967-w 10.1007/s10489-022-03422-6 10.1016/j.neucom.2017.04.060 10.1109/34.291440 10.1007/s00500-016-2189-8 10.1016/j.sigpro.2020.107915 10.1126/science.1127647 10.1016/j.jclepro.2020.122248 10.1007/s00500-016-2372-y 10.1109/JIOT.2018.2856241 10.1109/TMM.2018.2865834 10.1109/TGRS.2018.2890040 10.1016/j.neucom.2005.12.126 10.1109/TNN.2006.875977 10.1007/s13042-020-01234-z 10.1016/j.asoc.2019.04.019 10.1109/5.726791 10.1016/j.cosrev.2021.100379 |
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| SubjectTerms | Algorithms Artificial neural networks Autoencoder Classification Complexity Computational Intelligence Data Structures and Information Theory Deep learning Engineering Extreme learning machine Fisher criterion Machine learning Multilayers Original Article Regularization Representation learning Unsupervised learning |
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| Title | Multilayer Fisher extreme learning machine for classification |
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