Multilayer Fisher extreme learning machine for classification
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 b...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2022-10-01
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| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-022-00867-7 |
| _version_ | 1797840750572994560 |
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| author | Jie Lai Xiaodan Wang Qian Xiang Jian Wang Lei Lei |
| author_facet | Jie Lai Xiaodan Wang Qian Xiang Jian Wang Lei Lei |
| author_sort | Jie Lai |
| collection | DOAJ |
| description | 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). |
| first_indexed | 2024-04-09T16:19:46Z |
| format | Article |
| id | doaj.art-d10de9068658478a9a68daf87ca6ce5e |
| institution | Directory Open Access Journal |
| issn | 2199-4536 2198-6053 |
| language | English |
| last_indexed | 2024-04-09T16:19:46Z |
| publishDate | 2022-10-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj.art-d10de9068658478a9a68daf87ca6ce5e2023-04-23T11:32:57ZengSpringerComplex & Intelligent Systems2199-45362198-60532022-10-01921975199310.1007/s40747-022-00867-7Multilayer Fisher extreme learning machine for classificationJie Lai0Xiaodan Wang1Qian Xiang2Jian Wang3Lei Lei4College of Air and Missile Defense, Air Force Engineering UniversityCollege of Air and Missile Defense, Air Force Engineering UniversityCollege of Air and Missile Defense, Air Force Engineering UniversityCollege of Air and Missile Defense, Air Force Engineering UniversityCollege of Information and Navigation, Air Force Engineering UniversityAbstract 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).https://doi.org/10.1007/s40747-022-00867-7Extreme learning machineDeep learningRepresentation learningAutoencoderFisher criterion |
| spellingShingle | Jie Lai Xiaodan Wang Qian Xiang Jian Wang Lei Lei Multilayer Fisher extreme learning machine for classification Complex & Intelligent Systems Extreme learning machine Deep learning Representation learning Autoencoder Fisher criterion |
| title | Multilayer Fisher extreme learning machine for classification |
| title_full | Multilayer Fisher extreme learning machine for classification |
| title_fullStr | Multilayer Fisher extreme learning machine for classification |
| title_full_unstemmed | Multilayer Fisher extreme learning machine for classification |
| title_short | Multilayer Fisher extreme learning machine for classification |
| title_sort | multilayer fisher extreme learning machine for classification |
| topic | Extreme learning machine Deep learning Representation learning Autoencoder Fisher criterion |
| url | https://doi.org/10.1007/s40747-022-00867-7 |
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