Cross-domain topic transfer learning method based on multiple balance and feature fusion
In transfer learning, traditional homogeneous transfer learning assumes similar data and feature distributions between the source and target domains, focusing primarily on parameter sharing to enhance model performance. However, heterogeneous transfer learning for topic model, disparities in data an...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-05-01
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| Series: | Heliyon |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024167941 |
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| author | Zhenshun Xu Zhenbiao Wang Wenhao Zhang Zengjin Tang |
| author_facet | Zhenshun Xu Zhenbiao Wang Wenhao Zhang Zengjin Tang |
| author_sort | Zhenshun Xu |
| collection | DOAJ |
| description | In transfer learning, traditional homogeneous transfer learning assumes similar data and feature distributions between the source and target domains, focusing primarily on parameter sharing to enhance model performance. However, heterogeneous transfer learning for topic model, disparities in data and feature distributions lead to negative transfer, diminishing the effectiveness of topic extraction. This research explores alternative methods for heterogeneous transfer learning in topic model beyond traditional parameter sharing, seeking to maximize the utilization of source domain knowledge and features, mitigating the interference of negative transfer. We propose a novel approach for cross-domain topic transfer learning by combining feature fusion and balancing data and labels. Including applying dual-supervised techniques for handling label dependencies, employing function constraints and data enhancement to adjust data distributions, and utilizing feature fusion techniques to mitigate feature distribution disparities. Additionally, we introduce a topic knowledge distillation method, leveraging topics from the source domain to guide and optimize topic generation in the target domain. In practical applications, this method enhances animal disease topic mining by integrating feature knowledge from the animal diseases dataset and the 20 Newsgroups dataset. The experiments verify the effectiveness of this method, bridging gaps in topic generation and advancing the application of topic modeling in complex and dynamic contexts. |
| format | Article |
| id | doaj-art-88dbe391ef17442ea8e17496ed3bc664 |
| institution | DOAJ |
| issn | 2405-8440 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-88dbe391ef17442ea8e17496ed3bc6642025-08-20T03:19:56ZengElsevierHeliyon2405-84402025-05-011110e4076310.1016/j.heliyon.2024.e40763Cross-domain topic transfer learning method based on multiple balance and feature fusionZhenshun Xu0Zhenbiao Wang1Wenhao Zhang2Zengjin Tang3College of Compute Science and Engineering, North Minzu University, Yinchuan, China; The Key Laboratory of Images & Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan, China; Corresponding author. College of Compute Science and Engineering, North Minzu University, Yinchuan, China.College of Compute Science and Engineering, North Minzu University, Yinchuan, China; The Key Laboratory of Images & Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan, ChinaCollege of Compute Science and Engineering, North Minzu University, Yinchuan, China; The Key Laboratory of Images & Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan, ChinaCollege of Compute Science and Engineering, North Minzu University, Yinchuan, China; The Key Laboratory of Images & Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan, ChinaIn transfer learning, traditional homogeneous transfer learning assumes similar data and feature distributions between the source and target domains, focusing primarily on parameter sharing to enhance model performance. However, heterogeneous transfer learning for topic model, disparities in data and feature distributions lead to negative transfer, diminishing the effectiveness of topic extraction. This research explores alternative methods for heterogeneous transfer learning in topic model beyond traditional parameter sharing, seeking to maximize the utilization of source domain knowledge and features, mitigating the interference of negative transfer. We propose a novel approach for cross-domain topic transfer learning by combining feature fusion and balancing data and labels. Including applying dual-supervised techniques for handling label dependencies, employing function constraints and data enhancement to adjust data distributions, and utilizing feature fusion techniques to mitigate feature distribution disparities. Additionally, we introduce a topic knowledge distillation method, leveraging topics from the source domain to guide and optimize topic generation in the target domain. In practical applications, this method enhances animal disease topic mining by integrating feature knowledge from the animal diseases dataset and the 20 Newsgroups dataset. The experiments verify the effectiveness of this method, bridging gaps in topic generation and advancing the application of topic modeling in complex and dynamic contexts.http://www.sciencedirect.com/science/article/pii/S2405844024167941 |
| spellingShingle | Zhenshun Xu Zhenbiao Wang Wenhao Zhang Zengjin Tang Cross-domain topic transfer learning method based on multiple balance and feature fusion Heliyon |
| title | Cross-domain topic transfer learning method based on multiple balance and feature fusion |
| title_full | Cross-domain topic transfer learning method based on multiple balance and feature fusion |
| title_fullStr | Cross-domain topic transfer learning method based on multiple balance and feature fusion |
| title_full_unstemmed | Cross-domain topic transfer learning method based on multiple balance and feature fusion |
| title_short | Cross-domain topic transfer learning method based on multiple balance and feature fusion |
| title_sort | cross domain topic transfer learning method based on multiple balance and feature fusion |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024167941 |
| work_keys_str_mv | AT zhenshunxu crossdomaintopictransferlearningmethodbasedonmultiplebalanceandfeaturefusion AT zhenbiaowang crossdomaintopictransferlearningmethodbasedonmultiplebalanceandfeaturefusion AT wenhaozhang crossdomaintopictransferlearningmethodbasedonmultiplebalanceandfeaturefusion AT zengjintang crossdomaintopictransferlearningmethodbasedonmultiplebalanceandfeaturefusion |