Substructure correlation adaptation transfer learning method based on K-means clustering
Domain drifts severely affect the performance of traditional machine learning methods, and existing domain adaptive methods are mainly represented by adaptive adjustment cross-domain through global, class-level, or sample-level distribution adaptation.However, too coarse global matching and class-le...
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2023-03-01
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| Series: | Dianxin kexue |
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| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2023045/ |
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| _version_ | 1841530830963343360 |
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| author | Haoshuang LIU Yong ZHANG Yingbo CAO |
| author_facet | Haoshuang LIU Yong ZHANG Yingbo CAO |
| author_sort | Haoshuang LIU |
| collection | DOAJ |
| description | Domain drifts severely affect the performance of traditional machine learning methods, and existing domain adaptive methods are mainly represented by adaptive adjustment cross-domain through global, class-level, or sample-level distribution adaptation.However, too coarse global matching and class-level matching can lead to insufficient adaptation, and sample-level adaptation to noise can lead to excessive adaptation.A substructure correlation adaptation (SCOAD) transfer learning algorithm based on K-means clustering was proposed.Firstly, multiple subdomains of the source domain and the target domain were obtained by K-means clustering.Then, the matching of the second-order statistics of the subdomain center was sought.Finally, the target domain samples were classified by using the subdomain structure.The proposed method approach further improves the performance of knowledge transfer between the source and target domains on top of the traditional approach.Experimental results on common transfer learning datasets show the effectiveness of the proposed method. |
| format | Article |
| id | doaj-art-6c87e1b507e04ee68bde11bc279c9e35 |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2023-03-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-6c87e1b507e04ee68bde11bc279c9e352025-01-15T02:58:58ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012023-03-013912413459569897Substructure correlation adaptation transfer learning method based on K-means clusteringHaoshuang LIUYong ZHANGYingbo CAODomain drifts severely affect the performance of traditional machine learning methods, and existing domain adaptive methods are mainly represented by adaptive adjustment cross-domain through global, class-level, or sample-level distribution adaptation.However, too coarse global matching and class-level matching can lead to insufficient adaptation, and sample-level adaptation to noise can lead to excessive adaptation.A substructure correlation adaptation (SCOAD) transfer learning algorithm based on K-means clustering was proposed.Firstly, multiple subdomains of the source domain and the target domain were obtained by K-means clustering.Then, the matching of the second-order statistics of the subdomain center was sought.Finally, the target domain samples were classified by using the subdomain structure.The proposed method approach further improves the performance of knowledge transfer between the source and target domains on top of the traditional approach.Experimental results on common transfer learning datasets show the effectiveness of the proposed method.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2023045/transfer learningdomain adaptationsubstructural adaptationclustering |
| spellingShingle | Haoshuang LIU Yong ZHANG Yingbo CAO Substructure correlation adaptation transfer learning method based on K-means clustering Dianxin kexue transfer learning domain adaptation substructural adaptation clustering |
| title | Substructure correlation adaptation transfer learning method based on K-means clustering |
| title_full | Substructure correlation adaptation transfer learning method based on K-means clustering |
| title_fullStr | Substructure correlation adaptation transfer learning method based on K-means clustering |
| title_full_unstemmed | Substructure correlation adaptation transfer learning method based on K-means clustering |
| title_short | Substructure correlation adaptation transfer learning method based on K-means clustering |
| title_sort | substructure correlation adaptation transfer learning method based on k means clustering |
| topic | transfer learning domain adaptation substructural adaptation clustering |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2023045/ |
| work_keys_str_mv | AT haoshuangliu substructurecorrelationadaptationtransferlearningmethodbasedonkmeansclustering AT yongzhang substructurecorrelationadaptationtransferlearningmethodbasedonkmeansclustering AT yingbocao substructurecorrelationadaptationtransferlearningmethodbasedonkmeansclustering |