Discriminative Extreme Learning Machine with Cross-Domain Mean Approximation for Unsupervised Domain Adaptation
Extreme Learning Machine (ELM) is widely used in various fields because of its fast training and high accuracy. However, it does not primarily work well for Domain Adaptation (DA) in which there are many annotated data from auxiliary domain and few even no annotated data in target domain. In this pa...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2022/2463746 |
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| _version_ | 1850170664198078464 |
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| author | Shaofei Zang Xinghai Li Jianwei Ma Yongyi Yan Jinfeng Lv Yuan Wei |
| author_facet | Shaofei Zang Xinghai Li Jianwei Ma Yongyi Yan Jinfeng Lv Yuan Wei |
| author_sort | Shaofei Zang |
| collection | DOAJ |
| description | Extreme Learning Machine (ELM) is widely used in various fields because of its fast training and high accuracy. However, it does not primarily work well for Domain Adaptation (DA) in which there are many annotated data from auxiliary domain and few even no annotated data in target domain. In this paper, we propose a new variant of ELM called Discriminative Extreme Learning Machine with Cross-Domain Mean Approximation (DELM-CDMA) for unsupervised domain adaptation. It introduces Cross-Domain Mean Approximation (CDMA) into the hidden layer of ELM to reduce distribution discrepancy between domains for domain bias elimination, which is conducive to train a high accuracy ELM on annotated data from auxiliary domains for target tasks. Linear Discriminative Analysis (LDA) is also adopted to improve the discrimination of learned model and obtain higher accuracy. Moreover, we further provide a Discriminative Kernel Extreme Learning Machine with Cross-Domain Mean Approximation (DKELM-CDMA) as the kernelization extension of DELM-CDMA. Some experiments are performed to investigate the proposed approach, and the result shows that DELM-CDMA and DKELM-CDMA could effectively extend ELM suitable for domain adaptation and outperform ELM and many other domain adaptation approaches. |
| format | Article |
| id | doaj-art-9b32c9cfb67f48bf98050013eca95efd |
| institution | OA Journals |
| issn | 1099-0526 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-9b32c9cfb67f48bf98050013eca95efd2025-08-20T02:20:26ZengWileyComplexity1099-05262022-01-01202210.1155/2022/2463746Discriminative Extreme Learning Machine with Cross-Domain Mean Approximation for Unsupervised Domain AdaptationShaofei Zang0Xinghai Li1Jianwei Ma2Yongyi Yan3Jinfeng Lv4Yuan Wei5College of Information EngineeringCollege of Information EngineeringCollege of Information EngineeringCollege of Information EngineeringCollege of Information EngineeringCollege of Vehicle and Traffic EngineeringExtreme Learning Machine (ELM) is widely used in various fields because of its fast training and high accuracy. However, it does not primarily work well for Domain Adaptation (DA) in which there are many annotated data from auxiliary domain and few even no annotated data in target domain. In this paper, we propose a new variant of ELM called Discriminative Extreme Learning Machine with Cross-Domain Mean Approximation (DELM-CDMA) for unsupervised domain adaptation. It introduces Cross-Domain Mean Approximation (CDMA) into the hidden layer of ELM to reduce distribution discrepancy between domains for domain bias elimination, which is conducive to train a high accuracy ELM on annotated data from auxiliary domains for target tasks. Linear Discriminative Analysis (LDA) is also adopted to improve the discrimination of learned model and obtain higher accuracy. Moreover, we further provide a Discriminative Kernel Extreme Learning Machine with Cross-Domain Mean Approximation (DKELM-CDMA) as the kernelization extension of DELM-CDMA. Some experiments are performed to investigate the proposed approach, and the result shows that DELM-CDMA and DKELM-CDMA could effectively extend ELM suitable for domain adaptation and outperform ELM and many other domain adaptation approaches.http://dx.doi.org/10.1155/2022/2463746 |
| spellingShingle | Shaofei Zang Xinghai Li Jianwei Ma Yongyi Yan Jinfeng Lv Yuan Wei Discriminative Extreme Learning Machine with Cross-Domain Mean Approximation for Unsupervised Domain Adaptation Complexity |
| title | Discriminative Extreme Learning Machine with Cross-Domain Mean Approximation for Unsupervised Domain Adaptation |
| title_full | Discriminative Extreme Learning Machine with Cross-Domain Mean Approximation for Unsupervised Domain Adaptation |
| title_fullStr | Discriminative Extreme Learning Machine with Cross-Domain Mean Approximation for Unsupervised Domain Adaptation |
| title_full_unstemmed | Discriminative Extreme Learning Machine with Cross-Domain Mean Approximation for Unsupervised Domain Adaptation |
| title_short | Discriminative Extreme Learning Machine with Cross-Domain Mean Approximation for Unsupervised Domain Adaptation |
| title_sort | discriminative extreme learning machine with cross domain mean approximation for unsupervised domain adaptation |
| url | http://dx.doi.org/10.1155/2022/2463746 |
| work_keys_str_mv | AT shaofeizang discriminativeextremelearningmachinewithcrossdomainmeanapproximationforunsuperviseddomainadaptation AT xinghaili discriminativeextremelearningmachinewithcrossdomainmeanapproximationforunsuperviseddomainadaptation AT jianweima discriminativeextremelearningmachinewithcrossdomainmeanapproximationforunsuperviseddomainadaptation AT yongyiyan discriminativeextremelearningmachinewithcrossdomainmeanapproximationforunsuperviseddomainadaptation AT jinfenglv discriminativeextremelearningmachinewithcrossdomainmeanapproximationforunsuperviseddomainadaptation AT yuanwei discriminativeextremelearningmachinewithcrossdomainmeanapproximationforunsuperviseddomainadaptation |