Regularized manifold information extreme learning machine
By exploiting the thought of manifold learning and its theoretical method, a regularized manifold information ex-treme learning machine algorithm aimed to depict and fully utilize manifold information was proposed. The proposed algo-rithm exploited the geometry and discrimination manifold informatio...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2016-11-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2016213/ |
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| author | De-shan LIU Yong-he CHU De-qin YAN |
| author_facet | De-shan LIU Yong-he CHU De-qin YAN |
| author_sort | De-shan LIU |
| collection | DOAJ |
| description | By exploiting the thought of manifold learning and its theoretical method, a regularized manifold information ex-treme learning machine algorithm aimed to depict and fully utilize manifold information was proposed. The proposed algo-rithm exploited the geometry and discrimination manifold information of data to perform network of ELM. The proposed algorithm could overcome the problem of the overlap of information. Singular problems of inter-class and within-class were solved effectively by using maximum margin criterion. The problem of inadequate learning with limited samples was solved. In order to demonstrate the effectiveness, comparative experiments with ELM and the related update algorithms RAFELM, GELM were conducted using the commonly used image data. Experimental results show that the proposed algorithm can significantly improve the generalization performance of ELM and outperforms the related update algorithms. |
| format | Article |
| id | doaj-art-23824d6eefc447edaaafcff6514af087 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2016-11-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-23824d6eefc447edaaafcff6514af0872025-01-14T06:56:19ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2016-11-0137576759704544Regularized manifold information extreme learning machineDe-shan LIUYong-he CHUDe-qin YANBy exploiting the thought of manifold learning and its theoretical method, a regularized manifold information ex-treme learning machine algorithm aimed to depict and fully utilize manifold information was proposed. The proposed algo-rithm exploited the geometry and discrimination manifold information of data to perform network of ELM. The proposed algorithm could overcome the problem of the overlap of information. Singular problems of inter-class and within-class were solved effectively by using maximum margin criterion. The problem of inadequate learning with limited samples was solved. In order to demonstrate the effectiveness, comparative experiments with ELM and the related update algorithms RAFELM, GELM were conducted using the commonly used image data. Experimental results show that the proposed algorithm can significantly improve the generalization performance of ELM and outperforms the related update algorithms.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2016213/extreme learning machinegeometrymanifold informationmachine learning |
| spellingShingle | De-shan LIU Yong-he CHU De-qin YAN Regularized manifold information extreme learning machine Tongxin xuebao extreme learning machine geometry manifold information machine learning |
| title | Regularized manifold information extreme learning machine |
| title_full | Regularized manifold information extreme learning machine |
| title_fullStr | Regularized manifold information extreme learning machine |
| title_full_unstemmed | Regularized manifold information extreme learning machine |
| title_short | Regularized manifold information extreme learning machine |
| title_sort | regularized manifold information extreme learning machine |
| topic | extreme learning machine geometry manifold information machine learning |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2016213/ |
| work_keys_str_mv | AT deshanliu regularizedmanifoldinformationextremelearningmachine AT yonghechu regularizedmanifoldinformationextremelearningmachine AT deqinyan regularizedmanifoldinformationextremelearningmachine |