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Transfer Learning with LDA Topic Model

Transfer Learning with LDA Topic Model applies knowledge from a well-studied source domain to guide Latent Dirichlet Allocation inference on a data-scarce target domain. By injecting source-derived topic priors into the Dirichlet hyperparameters, the method produces coherent, domain-relevant topics even when target-domain text is limited, reducing the volume of labelled or unlabelled data required for meaningful results.

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Sources

  1. Chen, Z., Mukherjee, A., Liu, B., Hsu, M., Malas, M., & Wang, S. (2013). Leveraging multi-domain prior knowledge in topic models. In Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence (IJCAI-13), pp. 2071–2077. link ↗
  2. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022. link ↗

Related methods

Fine-Tuned LDA Topic ModelLDA Topic ModelTopic ModelingTransfer Learning with NMF Topic Model

Referenced by

Transfer Learning with NMF Topic Model

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ScholarGate — Transfer Learning with LDA Topic Model (Transfer Learning with Latent Dirichlet Allocation Topic Model). Retrieved 2026-06-04 from https://scholargate.app/tr/deep-learning/transfer-learning-with-lda-topic-model