Multisource transfer learning based on multiconcept and multispace for text classification

Qiaoli Qu; Yi Zhu; Jianhan Pan

doi:10.1117/12.3035241

11 July 2024 Multisource transfer learning based on multiconcept and multispace for text classification

Qiaoli Qu, Yi Zhu, Jianhan Pan

Proceedings Volume 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024); 132102J (2024) https://doi.org/10.1117/12.3035241
Event: Third International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 2024, Wuhan, China

Abstract

Many multi-source transfer learning methods use the latent attributes between source domains to mitigate interference. However, these strategies have some shortcomings. Strategies that only use commonality may lose valuable latent information specific to a single source domain when extracting knowledge. Strategies that focus on specificity are limited by the differences between source domains, so they cannot effectively integrate different knowledge. For strategies that include both attributes, they only explore the knowledge scattered in the original source domains, and do not attempt to explore additional knowledge, which can fill the learning gap and promote knowledge fusion. Based on the above problems, this paper proposes a multi-source transfer learning method that can deeply mine the commonality and characteristics of multiple source domains and fully integrate all the latent information by reconstructing the multisource latent feature space and combining high-level concept learning. Finally, we conduct a large number of experiments to verify the effectiveness and superiority of the algorithm.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qiaoli Qu, Yi Zhu, and Jianhan Pan "Multisource transfer learning based on multiconcept and multispace for text classification", Proc. SPIE 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024), 132102J (11 July 2024); https://doi.org/10.1117/12.3035241

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