So sánh phương pháp

	Mô hình Chủ đề LDA Tinh chỉnh	Mô hình Chủ đề NMF
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2003 (base); adaptation practice ~2010s	1999
Người khởi xướng	Blei, D. M., Ng, A. Y., & Jordan, M. I. (base LDA); domain adaptation via online/warm-start LDA	Lee, D. D. & Seung, H. S.
Loại	Probabilistic generative topic model (fine-tuned / domain-adapted)	Matrix factorization / unsupervised topic model
Công trình gốc	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.	Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791.
Tên gọi khác	Domain-Adapted LDA, Adapted LDA, LDA Fine-Tuning, Online LDA Fine-Tuning	NMF, Non-negative Matrix Factorization, NMF for Topic Modeling, NNMF Topic Model
Liên quan	5	4
Tóm tắt	Fine-Tuned LDA adapts a Latent Dirichlet Allocation model trained on a large general corpus to a specific target domain by continuing inference on domain-specific documents. Rather than fitting LDA from scratch, the pre-trained topic-word distributions are used as an informed starting point, enabling the model to discover coherent domain topics faster and with less data than training cold.	Non-negative Matrix Factorization (NMF) is an unsupervised matrix decomposition method that discovers latent topics in a text corpus by factoring a document-term matrix into two non-negative matrices — one encoding topic-word weights, the other document-topic weights. The non-negativity constraint yields parts-based, additive representations that tend to produce clean, interpretable topics.